CN109916423A - Intelligent navigation equipment and its route planning method and automatic driving vehicle - Google Patents
Intelligent navigation equipment and its route planning method and automatic driving vehicle Download PDFInfo
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Abstract
This application involves intelligent navigation equipment technical fields, a kind of intelligent navigation equipment and its route planning method are provided, and automatic driving vehicle, when detecting that user gives expression to row planning intention, intelligent navigation equipment obtains more wheel voice signals of user's input, the trip planning of the user is identified then according to more wheel voice signals, and then navigation relevant to the trip planning is obtained according to trip planning and is recorded, history trip habit, route selection preference, at least two composite factor in current road conditions and current slot restricted driving information, it finally can be according to the composite factor, it provides the route planning suggestion to match with the trip planning and navigation information is provided.The application can facilitate user to carry out speech search to arrive the route planning for being suitble to user, and no longer need user to carry out manual screening, while human-computer interaction process facilitates naturally, easy to operate, largely improves user experience.
Description
Technical field
This application involves navigation equipment technical fields, and in particular to a kind of route planning side based on more wheel interactive voices
Method, a kind of intelligent navigation equipment and a kind of automatic driving vehicle.
Background technique
Global positioning system (GlobalPositioningSystem, abbreviation GPS) is the U.S. since the 1970s
It develops, was built up comprehensively in 1994, there is the satellite of new generation in sea, land and sky comprehensive real-time three-dimensional navigation and stationkeeping ability
Navigation and positioning system.GPS system cannot be only used for measurement, navigation, it may also be used for when testing the speed, surveying.By GPS receiver to defend
Star signal is accurately positioned, and can learn the information such as travel route, position, speed, the height above sea level of vehicle.
GIS-Geographic Information System (GeographicalInformationSystem, abbreviation GIS) is from the end of the fifties and the sixties
Just start to occur, is the mankind in production practices activity, the software system gradually generated to describe geography information related to handling
System.It is handled the spatial data with geographical feature using computer as means, is served as theme with a spatial information, will
Various other associated spatial positional informations combine, and have the function of such as to acquire, manage, analyze and express data.
Secondly, the data of GIS processing all have direct or indirect relationship with geography information.Geography information is the property in relation to geographical entity
Matter, feature, the characterization of motion state and all useful knowledge, and geodata is then relationship between various geographical features and phenomenon
Symbolic Representation.
In recent years, GPS technology is other than applying in traditional vehicle-mounted antitheft counter raid device, in conjunction with generalized information system, vehicle-mounted leads
The application of boat instrument is also increasingly extensive.With the development of the automobile industry and automotive electronics market graduallys mature, and vehicle mounted guidance produces
Product have begun to take shape.In American-European and Japan, such automatic navigator use is commonplace, and current country's GPS auto navigation produces
Product also have the market trend of sharp increase.
Android (Android) is the mobile terminal operating system based on Linux platform, is increased income in recent years by it, efficiently
Etc. advantages and manufacturer promotion, won vast user group.It is big absolutely by mobile terminal of operating system itself of Android
Majority has the function of the calculation processing power of high speed and GPS, has bright future in the expansion of function.
Meanwhile speech recognition and man machine language's interaction are also in development in an all-round way, existing various voice assistant class applications
(Application；Hereinafter referred to as: APP), in mode of operation, the triggering of recording is by key, and after recording, machine is broadcast
Repayment case cannot record when broadcasting answer.That is, existing voice assistant class APP can only carry out half-duplex operation, i.e.,
When machine is broadcasted, user is mute, and when user speaks, machine cannot be broadcasted.
But in the prior art, user can not utilize the direct speech trigger of existing navigation system and search destination,
Or user is needed to do it yourself to screen during manual search, moreover, existing man machine language's interactive mode uses
It is very inconvenient to come, and each question-response requires user intervention, and cumbersome, man-machine interaction mode is also very unnatural, user's body
Degree of testing is poor.
Summary of the invention
The purpose of the application is, provides a kind of intelligent navigation equipment and its route planning method and automatic driving vehicle,
It can solve above-mentioned technical problem, user can be facilitated to carry out speech search to the route planning for being suitble to user, and no longer
User is needed to carry out manual screening, while human-computer interaction process is convenient naturally, easy to operate, largely improves user's body
It tests.
In order to solve the above technical problems, the application provides a kind of route planning method based on more wheel interactive voices, it is described
Route planning method includes:
When detecting that user gives expression to row planning intention, intelligent navigation equipment obtains more wheel voices letter of user's input
Number；
The trip planning of the user is identified according to more wheel voice signals；
Navigation record relevant to the trip planning, history trip habit, route choosing are obtained according to trip planning
Select at least two composite factor in preference, current road conditions and current slot restricted driving information；
According to the composite factor, provides the route planning suggestion to match with the trip planning and navigation letter is provided
Breath.
Wherein, described according to the composite factor, it provides the route planning suggestion to match with the trip planning and mentions
It the step of for navigation information, specifically includes:
Priority ranking is carried out to multiple composite factors, to provide multiple route suggestions and provide a plurality of accordingly lead
It navigates information, wherein the priority ranking includes preferential by navigation record, preferential, excellent by route preferences by history trip habit
First, preferentially and preferential by current slot restricted driving information by current road conditions.
Wherein, it is described when detecting that user gives expression to row planning intention the step of, specifically include:
Intelligent navigation equipment has detected whether touch-control input words and phrases relevant to trip planning, wherein plans phase with trip
The words and phrases of pass include " I wants to go to ", " going to certain destination ", " how is certain destination ", " someone family ", " hotel Qu Mou has a meal ",
" going to company " and " going to certain sight spot ".
Wherein, described to identify that the trip of the user is planned according to more wheel voice signals, it specifically includes:
Receive more wheel voice signals of input；
The voice characteristics information of more wheel voice signals is extracted in framing, generates more wheels according to voice characteristics information and acoustic model
The recognition result of voice signal；
According to recognition result and preset the mute detection algorithm Preliminary detection sound ends for taking turns voice signals out more；
Calculate the confidence information of more wheel voice signals；
The semantic information of the more wheel voice signals of parsing；
The corresponding speech analysis result of more wheel voice signals is obtained according to confidence information and semantic information；
The trip planning of the user is identified according to speech analysis result.
Wherein, the recognition result that more wheel voice signals are generated according to voice characteristics information and acoustic model, it is specific to wrap
It includes:
After generating voice characteristics information, take turns voice signal in each modeling list using every frame is calculated based on acoustic model more
Likelihood value in member obtains optimum state metastasis sequence and its corresponding word sequence by dynamic programming algorithm, and will be acquired
Optimum state metastasis sequence and its corresponding word sequence as recognition result.
Wherein, the acoustic model is acoustic model neural network based, using the acoustics neural network based
Model identifies the voice characteristics information.
Wherein, the step of confidence information of voice signal, is taken turns in the calculating more, specifically includes:
Take turns voice according to the signal-to-noise ratio computation of recognition result, the sound end of more wheel voice signals and more wheel voice signals more
The confidence information of signal.
Wherein, described according to recognition result, the signal-to-noise ratio meter of the sound end of more wheel voice signals and more wheel voice signals
The confidence information for calculating more wheel voice signals, specifically includes:
Based on recognition result, the acoustics posterior probability of each word between sound end is calculated, wherein calculate sound end
Between the formula of acoustics posterior probability of k-th of word include:
Wherein P (X) is the acoustics posterior probability of k-th of word in more wheel voice signals, and the word is corresponding when p (m | x) is t frame
The likelihood value of modeling unit,When for t frame the likelihood value of all modeling units and, T (X) is the duration of the word；
According to the acoustics posterior probability of each word between sound end and the corresponding confidence level of each word of signal-to-noise ratio computation
Information.
Wherein, each word of the acoustics posterior probability and signal-to-noise ratio computation according to each word between sound end is corresponding
Confidence information, specifically include:
Acoustics posterior probability and signal-to-noise ratio based on current word, calculate the confidence level CM (X) of current word, and formula includes:
CM (X)=w*P (X)+(1-w) * SNR (X)
Wherein 0≤w≤1, w are weight coefficient；
Calculate the formula for taking turns the corresponding confidence level of voice signals includes: more
Wherein, t (x) indicates that the duration of n-th of word is long, and CM (x) indicates the confidence level of n-th of word；
Read group total is carried out to the corresponding confidence level of each word, acquires the confidence information of more wheel voice signals.
In order to solve the above technical problems, the application also provides a kind of intelligent navigation equipment, the intelligent navigation equipment includes
Processor, the processor is for executing program data, to realize the above-mentioned route planning method based on more wheel interactive voices
The step of.
In order to solve the above technical problems, the application also provides a kind of automatic driving vehicle, it is provided with processor, the processing
When device executes program data, the step of for realizing the above-mentioned route planning method based on more wheel interactive voices.
The application intelligent navigation equipment and its route planning method and automatic driving vehicle are detecting that user gives expression to
When row planning intention, intelligent navigation equipment obtains more wheel voice signals of user's input, then according to more wheel voice signals
Identify the trip planning of the user, and then recorded according to trip planning acquisition navigation relevant to the trip planning,
At least two synthesis in history trip habit, route selection preference, current road conditions and current slot restricted driving information
Factor finally can provide the route planning suggestion to match with the trip planning and provide and lead according to the composite factor
Boat information.In this way, the application can facilitate user to carry out speech search to the route planning for being suitble to user, and not
User is needed to carry out manual screening again, while human-computer interaction process is convenient naturally, easy to operate, largely improves user
Experience.The application can be realized full voice interactive process, be manually operated without user.
Above description is only the general introduction of technical scheme, in order to better understand the technological means of the application,
And it can be implemented in accordance with the contents of the specification, and in order to allow the above and other objects, features and advantages of the application can
It is clearer and more comprehensible, it is special below to lift preferred embodiment, and cooperate attached drawing, detailed description are as follows.
Detailed description of the invention
Fig. 1 is the flow diagram of route planning method of the application based on more wheel interactive voices.
Fig. 2 is the module diagram of the application intelligent navigation equipment.
Specific embodiment
Further to illustrate that the application is the technical means and efficacy reaching predetermined application purpose and being taken, below in conjunction with
Attached drawing and preferred embodiment, to the intelligent navigation equipment proposed according to the application and its based on the route plannings for taking turns interactive voices more
Method and the specific embodiment of automatic driving vehicle, method, step, structure, feature and its effect, detailed description are as follows.
Aforementioned and other technology contents, feature and effect in relation to the application refer to the preferable reality of schema in following cooperation
Applying in the detailed description of example can clearly appear from.By the explanation of specific embodiment, when can be that reach predetermined mesh to the application
The technical means and efficacy taken be able to more deeply and it is specific understand, however institute's accompanying drawings are only to provide with reference to and say
It is bright to be used, not it is used to limit the application.
Referring to Fig. 1, Fig. 1 is the flow diagram of route planning method of the application based on more wheel interactive voices, this reality
The route planning method applied based on more wheel interactive voices described in mode includes but is not limited to the following steps.
Step S101, when detecting that user gives expression to row planning intention, intelligent navigation equipment obtains the more of user's input
Take turns voice signal.
Wherein, the step of step S101 is when detecting that user gives expression to row planning intention, can specifically include: intelligence
Energy navigation equipment has detected whether touch-control input words and phrases relevant to trip planning, wherein words and phrases packet relevant to trip planning
Include " I wants to go to ", " going to certain destination ", " how is certain destination ", " someone family ", " hotel Qu Mou has a meal ", " going to company " and
" going to certain sight spot ".Specifically, for example, user says " I wants to go to DiWang Building " " I wants to go to friend family " etc. with voice.
Step S102 identifies that the trip of the user is planned according to more wheel voice signals.
Wherein, the step S102 identifies that the trip of the user is planned according to more wheel voice signals, specifically can be with
It include: that keyword identification is carried out to more wheel voice signals；Based on context recognition result adjusts keyword；After adjustment
Keyword identify the user trip planning.
It should be strongly noted that the step S102 identifies that the user's goes out professional etiquette according to more wheel voice signals
It draws, can also specifically comprise the following processes:
S21 receives more wheel voice signals of input；
S22, framing are extracted the voice characteristics information of more wheel voice signals, are generated according to voice characteristics information and acoustic model
The recognition result of more wheel voice signals；
S23 according to recognition result and presets the mute detection algorithm Preliminary detection sound ends for taking turns voice signals out more；
S24 calculates the confidence information of more wheel voice signals；
S25 parses the semantic information of more wheel voice signals；
S26 obtains the corresponding speech analysis result of more wheel voice signals according to confidence information and semantic information；
S27 identifies that the trip of the user is planned according to speech analysis result.
In the present embodiment, the S22 generates the knowledge of more wheel voice signals according to voice characteristics information and acoustic model
Not as a result, can specifically include: after generating voice characteristics information, taking turns voice signal using every frame is calculated based on acoustic model more
Likelihood value in each modeling unit obtains optimum state metastasis sequence and its corresponding word order by dynamic programming algorithm
Column, and using obtained optimum state metastasis sequence and its corresponding word sequence as recognition result.
It is noted that the acoustic model is the sound based on neural network (DNN, Deep Neural Networks)
Model is learned, the voice characteristics information is identified using the acoustic model neural network based.
It should be noted that the S24 calculates the step of confidence information of more wheel voice signals, can specifically include:
Take turns setting for voice signal according to the signal-to-noise ratio computation of recognition result, the sound end of more wheel voice signals and more wheel voice signals more
Confidence information.
Furthermore, described according to recognition result, the letter of the sound end of more wheel voice signals and more wheel voice signals
It makes an uproar than the confidence information for calculating more wheel voice signals, can specifically include:
Based on recognition result, the acoustics posterior probability of each word between sound end is calculated, wherein calculate sound end
Between the formula of acoustics posterior probability of k-th of word include:
Wherein, P (X) is the acoustics posterior probability of k-th of word in more wheel voice signals, the word pair when p (m | x) is t frame
The likelihood value of modeling unit is answered,When for t frame the likelihood value of all modeling units and, T (X) is the duration of the word；
According to the acoustics posterior probability of each word between sound end and the corresponding confidence level of each word of signal-to-noise ratio computation
Information.
In the present embodiment, the acoustics posterior probability and signal-to-noise ratio computation according to each word between sound end
The corresponding confidence information of each word, can specifically include:
S31, acoustics posterior probability and signal-to-noise ratio based on current word calculate the confidence level CM (X) of current word, formula packet
It includes:
CM (X)=w*P (X)+(1-w) * SNR (X)
Wherein 0≤w≤1, w are weight coefficient；
S32, the formula for calculating the corresponding confidence level of more wheel voice signals include:
Wherein, t (x) indicates that the duration of n-th of word is long, and CM (x) indicates the confidence level of n-th of word；
S33 carries out read group total to the corresponding confidence level of each word, acquires the confidence level letter of more wheel voice signals
Breath.
Understandable to be, the application can be by two dimensions of confidence information and semantic information to more wheel voice signals
Determined, can effectively determine that whether resolved voice signals of taking turns are correct, revert statement is accurate when improving human-computer interaction more
Property, promote user experience.
Step S103 obtains navigation record relevant to the trip planning according to trip planning, history trip is practised
At least two composite factor in used, route selection preference, current road conditions and current slot restricted driving information.
Step S104 provides the route planning suggestion to match with the trip planning and mentions according to the composite factor
For navigation information.
It should be noted that the step S104, which according to the composite factor, is provided, goes on a journey what planning matched with described
Route planning suggestion and the step of provide navigation information, can specifically include: carrying out priority rows to multiple composite factors
Sequence, to provide multiple route suggestions and provide corresponding a plurality of navigation information, wherein the priority ranking includes by navigation note
Record is preferential, preferential, preferential by route preferences by history trip habit, preferential by current road conditions and restrict driving letter by current slot
Breath is preferential.
For example, when user language says the input such as " I will remove xx ", navigation can be by taking turns the side of voice dialogue more
Formula helps user to select current most quick comfortable in conjunction with history the trip habit, route selection preference, current road conditions of user
Traffic path.Wherein, mostly wheel interactive voices for example " it is now overhead a little stifled above, to walk ground and have a try? " -> " Metro is big
Parking lot near tall building has been fully parked with, and wants to be parked near star trip city, then take a windward driving and go over? " etc..
The application intelligent navigation equipment and its route planning method and automatic driving vehicle are detecting that user gives expression to
When row planning intention, intelligent navigation equipment obtains more wheel voice signals of user's input, then according to more wheel voice signals
Identify the trip planning of the user, and then recorded according to trip planning acquisition navigation relevant to the trip planning,
At least two synthesis in history trip habit, route selection preference, current road conditions and current slot restricted driving information
Factor finally can provide the route planning suggestion to match with the trip planning and provide and lead according to the composite factor
Boat information.In this way, the application can facilitate user to carry out speech search to the route planning for being suitble to user, and not
User is needed to carry out manual screening again, while human-computer interaction process is convenient naturally, easy to operate, largely improves user
Experience.The application can be realized full voice interactive process, be manually operated without user.
Please refer to figure 2, and Fig. 2 is the module diagram of one embodiment of the application intelligent navigation equipment, the application intelligence
Energy navigation equipment may include processor 21, memory 22 and display 23, wherein the intelligent navigation equipment can be navigation
Instrument, mobile phone, tablet computer, wearable device and in-vehicle navigation apparatus etc., are not limited thereto.
For the processor 21 for executing program data, the memory 22 can be used for storing program data, described aobvious
Show that device 23 is displayed for user interface, for example, display map, navigation, route and various man-machine interfaces.
In the present embodiment, the processor 21 is for when executing program data, realization to take turns interactive voices based on more
Route planning method the step of including but not limited to following examples.
The processor 21 obtains more wheel voices letter of user's input when detecting that user gives expression to row planning intention
Number.
Wherein, the step of processor 21 is when detecting that user gives expression to row planning intention, can specifically include: institute
It states processor 21 and has detected whether touch-control input words and phrases relevant to trip planning, wherein words and phrases packet relevant to trip planning
Include " I wants to go to ", " going to certain destination ", " how is certain destination ", " someone family ", " hotel Qu Mou has a meal ", " going to company " and
" going to certain sight spot ".Specifically, for example, user says " I is hungry " " I want to look for a little nice " etc. with voice.
The processor 21 identifies that the trip of the user is planned according to more wheel voice signals.
Wherein, the processor 21 identifies that the trip of the user is planned according to more wheel voice signals, specifically can be with
It include: that keyword identification is carried out to more wheel voice signals；Based on context recognition result adjusts keyword；After adjustment
Keyword identify the user trip planning.
It should be strongly noted that the processor 21 identifies that the user's goes out professional etiquette according to more wheel voice signals
It draws, can also specifically comprise the following processes:
S21 receives more wheel voice signals of input；
S22, framing are extracted the voice characteristics information of more wheel voice signals, are generated according to voice characteristics information and acoustic model
The recognition result of more wheel voice signals；
S23 according to recognition result and presets the mute detection algorithm Preliminary detection sound ends for taking turns voice signals out more；
S24 calculates the confidence information of more wheel voice signals；
S25 parses the semantic information of more wheel voice signals；
S26 obtains the corresponding speech analysis result of more wheel voice signals according to confidence information and semantic information；
S27 identifies that the trip of the user is planned according to speech analysis result.
In the present embodiment, processor 21 described in the S22 generates more according to voice characteristics information and acoustic model
The recognition result for taking turns voice signal, can specifically include: every using being calculated based on acoustic model after generating voice characteristics information
Frame takes turns likelihood value of the voice signal in each modeling unit more, by dynamic programming algorithm obtain optimum state metastasis sequence and
Its corresponding word sequence, and using obtained optimum state metastasis sequence and its corresponding word sequence as recognition result.
It is noted that the acoustic model is acoustic model neural network based, nerve net is based on using described
The acoustic model of network identifies the voice characteristics information.
It should be noted that the S24 calculates the step of confidence information of more wheel voice signals, can specifically include:
The processor 21 is taken turns more according to the signal-to-noise ratio computation of recognition result, the sound end of more wheel voice signals and more wheel voice signals
The confidence information of voice signal.
Furthermore, the processor 21 takes turns the sound end of voice signal more according to recognition result, and takes turns voice more
The signal-to-noise ratio computation of signal takes turns the confidence information of voice signal more, can specifically include:
The processor 21 is based on recognition result, calculates the acoustics posterior probability of each word between sound end, wherein
The formula of acoustics posterior probability for calculating k-th of word between sound end includes:
Wherein, P (X) is the acoustics posterior probability of k-th of word in more wheel voice signals, the word pair when p (m | x) is t frame
The likelihood value of modeling unit is answered,When for t frame the likelihood value of all modeling units and, T (X) is the duration of the word；
The processor 21 is according to the acoustics posterior probability and each word of signal-to-noise ratio computation of each word between sound end
Corresponding confidence information.
In the present embodiment, the processor 21 is according to the acoustics posterior probability and letter of each word between sound end
It makes an uproar than calculating the corresponding confidence information of each word, can specifically include:
S31, acoustics posterior probability and signal-to-noise ratio of the processor 21 based on current word, calculates the confidence level of current word
CM (X), formula include:
CM (X)=w*P (X)+(1-w) * SNR (X)
Wherein 0≤w≤1, w are weight coefficient；
S32, the formula that the processor 21 calculates the corresponding confidence level of more wheel voice signals include:
Wherein, t (x) indicates that the duration of n-th of word is long, and CM (x) indicates the confidence level of n-th of word；
S33, the processor 21 carry out read group total to the corresponding confidence level of each word, acquire more wheel voice signals
Confidence information.
Understandable to be, the application can be by two dimensions of confidence information and semantic information to more wheel voice signals
Determined, can effectively determine that whether resolved voice signals of taking turns are correct, revert statement is accurate when improving human-computer interaction more
Property, promote user experience.
The processor 21 obtains navigation record relevant to the trip planning according to trip planning, history goes out
At least two composite factor in row habit, route selection preference, current road conditions and current slot restricted driving information.
The processor 21 provides the route planning suggestion to match with the trip planning according to the composite factor
And provide navigation information.
It should be noted that the processor 21, which according to the composite factor, is provided, goes on a journey what planning matched with described
Route planning suggestion and the step of provide navigation information, can specifically include: the processor 21 is to multiple composite factors
Priority ranking is carried out, to provide multiple route suggestions and provide corresponding a plurality of navigation information, wherein the priority ranking
Including preferential by navigation record, preferential, preferential by route preferences by history trip habit, preferential and by current by current road conditions
Period restricted driving information is preferential.
For example, when user language says the input such as " I will remove xx ", navigation can be by taking turns the side of voice dialogue more
Formula helps user to select current most quick comfortable in conjunction with history the trip habit, route selection preference, current road conditions of user
Traffic path, for example, mostly wheel interactive voice for example " it is now overhead a little stifled above, to walk ground and have a try? " -> " Metro is big
Parking lot near tall building has been fully parked with, and wants to be parked near star trip city, then take a windward driving and go over? " etc..
The application intelligent navigation equipment and its route planning method and automatic driving vehicle are detecting that user gives expression to
When row planning intention, intelligent navigation equipment obtains more wheel voice signals of user's input, then according to more wheel voice signals
Identify the trip planning of the user, and then recorded according to trip planning acquisition navigation relevant to the trip planning,
At least two synthesis in history trip habit, route selection preference, current road conditions and current slot restricted driving information
Factor finally can provide the route planning suggestion to match with the trip planning and provide and lead according to the composite factor
Boat information.In this way, the application can facilitate user to carry out speech search to the route planning for being suitble to user, and not
User is needed to carry out manual screening again, while human-computer interaction process is convenient naturally, easy to operate, largely improves user
Experience.The application can be realized full voice interactive process, be manually operated without user.
In order to solve the above technical problems, the application also provides a kind of automatic driving vehicle, be provided with processor, memory and
Display screen etc., when the processor executes program data, for realizing the above-mentioned route planning side based on more wheel interactive voices
The step of method.Wherein, specific implementation please refers to the associated description of preceding embodiment, is easy knot in those skilled in the art
In the range of reasonable solution, do not repeat.
The automatic driving vehicle of the application, can be the unpiloted oil gas vehicle of pure automatic intelligent, electric vehicle,
It can be semi-automatic unpiloted oil gas vehicle, electric vehicle, intelligent navigation equipment described in Fig. 2 can be set as auxiliary
Operating system is helped, full automatic voice operation navigation may be implemented.
For the processor for executing program data, the memory can be used for storing program data, the display
It can be the display screen on automatic driving vehicle, for showing user interface, for example, display map, navigation, route and various
Man-machine interface.
In the present embodiment, the processor is for when executing program data, realization based on more wheel interactive voices
The step of route planning method including but not limited to following examples.
The processor obtains more wheel voice signals of user's input when detecting that user gives expression to row planning intention.
Wherein, the step of processor is when detecting that user gives expression to row planning intention, can specifically include: described
Processor has detected whether touch-control input words and phrases relevant to trip planning, wherein words and phrases relevant to trip planning include " I
Want to go to ", " going to certain destination ", " how is certain destination ", " someone family ", " hotel Qu Mou has a meal ", " going to company " and " go certain
Sight spot ".Specifically, for example, user says " I is hungry " " I want to look for a little nice " etc. with voice.
The processor identifies that the trip of the user is planned according to more wheel voice signals.
Wherein, the processor identifies that the trip of the user is planned according to more wheel voice signals, specifically can wrap
It includes: keyword identification is carried out to more wheel voice signals；Based on context recognition result adjusts keyword；According to adjusted
Keyword identifies the trip planning of the user.
It should be strongly noted that the processor identifies that the user's goes out professional etiquette according to more wheel voice signals
It draws, can also specifically comprise the following processes:
S21 receives more wheel voice signals of input；
S22, framing are extracted the voice characteristics information of more wheel voice signals, are generated according to voice characteristics information and acoustic model
The recognition result of more wheel voice signals；
S23 according to recognition result and presets the mute detection algorithm Preliminary detection sound ends for taking turns voice signals out more；
S24 calculates the confidence information of more wheel voice signals；
S25 parses the semantic information of more wheel voice signals；
S26 obtains the corresponding speech analysis result of more wheel voice signals according to confidence information and semantic information；
S27 identifies that the trip of the user is planned according to speech analysis result.
In the present embodiment, processor described in the S22 generates more wheels according to voice characteristics information and acoustic model
The recognition result of voice signal, can specifically include: after generating voice characteristics information, calculate every frame using based on acoustic model
Likelihood values of more wheel voice signals in each modeling unit, by dynamic programming algorithm obtain optimum state metastasis sequence and its
Corresponding word sequence, and using obtained optimum state metastasis sequence and its corresponding word sequence as recognition result.
It is noted that the acoustic model is acoustic model neural network based, nerve net is based on using described
The acoustic model of network identifies the voice characteristics information.
It should be noted that the S24 calculates the step of confidence information of more wheel voice signals, can specifically include:
The processor takes turns language according to the signal-to-noise ratio computation of recognition result, the sound end of more wheel voice signals and more wheel voice signals more
The confidence information of sound signal.
Furthermore, the processor is according to recognition result, the sound end and more wheel voice letters of more wheel voice signals
Number signal-to-noise ratio computation more take turns voice signal confidence information, can specifically include:
The processor is based on recognition result, calculates the acoustics posterior probability of each word between sound end, wherein meter
The formula of acoustics posterior probability for calculating k-th of word between sound end includes:
Wherein, P (X) is the acoustics posterior probability of k-th of word in more wheel voice signals, the word pair when p (m | x) is t frame
The likelihood value of modeling unit is answered,When for t frame the likelihood value of all modeling units and, T (X) is the duration of the word；
The processor is according to the acoustics posterior probability and each word pair of signal-to-noise ratio computation of each word between sound end
The confidence information answered.
In the present embodiment, the processor is according to the acoustics posterior probability and noise of each word between sound end
Than calculating the corresponding confidence information of each word, can specifically include:
S31, acoustics posterior probability and signal-to-noise ratio of the processor based on current word, calculates the confidence level CM of current word
(X), formula includes:
CM (X)=w*P (X)+(1-w) * SNR (X)
Wherein 0≤w≤1, w are weight coefficient；
S32, the formula that the processor calculates the corresponding confidence level of more wheel voice signals include:
Wherein, t (x) indicates that the duration of n-th of word is long, and CM (x) indicates the confidence level of n-th of word；
S33, the processor carry out read group total to the corresponding confidence level of each word, acquire more wheel voice signals
Confidence information.
Understandable to be, the application can be by two dimensions of confidence information and semantic information to more wheel voice signals
Determined, can effectively determine that whether resolved voice signals of taking turns are correct, revert statement is accurate when improving human-computer interaction more
Property, promote user experience.
The processor obtains navigation record relevant to the trip planning according to trip planning, history is gone on a journey
At least two composite factor in habit, route selection preference, current road conditions and current slot restricted driving information.
The processor provides the route planning suggestion to match with the trip planning simultaneously according to the composite factor
Navigation information is provided.
It should be noted that the processor according to the composite factor, provides the road to match with the trip planning
Line planning proposal and the step of provide navigation information, can specifically include: the processor carries out multiple composite factors
Priority ranking, to provide multiple route suggestions and provide corresponding a plurality of navigation information, wherein the priority ranking includes
Preferentially, by history going on a journey by navigation record, habit is preferential, it is preferential to press route preferences, it is preferential to press current road conditions and presses current time
Section restricted driving information is preferential.
For example, when user language says the input such as " I will remove xx ", navigation can be by taking turns the side of voice dialogue more
Formula helps user to select current most quick comfortable in conjunction with history the trip habit, route selection preference, current road conditions of user
Traffic path, for example, mostly wheel interactive voice for example " it is now overhead a little stifled above, to walk ground and have a try? " -> " Metro is big
Parking lot near tall building has been fully parked with, and wants to be parked near star trip city, then take a windward driving and go over? " etc..
The application automatic driving vehicle, when detecting that user gives expression to row planning intention, intelligent navigation equipment, which obtains, to be used
More wheel voice signals of family input identify that the trip of the user is planned then according to more wheel voice signals, and then basis
The trip planning obtains navigation record relevant to the trip planning, history is gone on a journey habit, route selection preference, current road
At least two composite factor in condition and current slot restricted driving information can finally give according to the composite factor
The route planning suggestion to match is planned with the trip out and navigation information is provided.In this way, the application can be square
Just user carries out the route planning of speech search to suitable user, and no longer needs user to carry out manual screening, while man-machine
Interactive process is convenient naturally, easy to operate, largely improves user experience.The application can be realized full voice and interact
Journey is manually operated without user.
It should be noted that all the embodiments in this specification are described in a progressive manner, each embodiment weight
Point explanation is the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other.
For terminal class embodiment, since it is basically similar to the method embodiment, so being described relatively simple, related place ginseng
See the part explanation of embodiment of the method.
The above is only the preferred embodiment of the application, not makes any form of restriction to the application, though
Right the application has been disclosed in a preferred embodiment above, however is not limited to the application, any technology people for being familiar with this profession
Member, is not departing within the scope of technical scheme, when the technology contents using the disclosure above make a little change or modification
For the equivalent embodiment of equivalent variations, but all technical spirits pair without departing from technical scheme content, according to the application
Any simple modification, equivalent change and modification made by above embodiments, in the range of still falling within technical scheme.
Claims (11)
1. a kind of route planning methods based on more wheel interactive voices, which is characterized in that the route planning method includes:
When detecting that user gives expression to row planning intention, intelligent navigation equipment obtains more wheel voice signals of user's input；
The trip planning of the user is identified according to more wheel voice signals；
Navigation record relevant to the trip planning is obtained according to trip planning, history trip is accustomed to, route selection is inclined
At least two composite factor in good, current road conditions and current slot restricted driving information；
According to the composite factor, provides the route planning suggestion to match with the trip planning and navigation information is provided.
2. route planning method according to claim 1, which is characterized in that it is described according to the composite factor, provide with
The step of trip plans the route planning suggestion to match and provides navigation information, specifically includes:
Priority ranking is carried out to multiple composite factors, to provide multiple route suggestions and provide corresponding a plurality of navigation letter
Breath, wherein the priority ranking include it is preferential by navigation record, preferential, preferential by route preferences by history trip habit, press
Current road conditions are preferential and preferential by current slot restricted driving information.
3. route planning method according to claim 2, which is characterized in that it is described detect user give expression to professional etiquette draw
The step of when intention, specifically includes:
Intelligent navigation equipment has detected whether touch-control or more wheel voice inputs words and phrases relevant to trip planning, wherein with trip
Plan that relevant words and phrases include " I wants to go to ", " going to certain destination ", " how is certain destination ", " someone family ", " hotel Qu Mou
Have a meal ", " going to company " and " going to certain sight spot ".
4. route planning method according to claim 1-3, which is characterized in that described according to more wheel voices
The trip of user described in signal identification is planned, is specifically included:
Receive more wheel voice signals of input；
The voice characteristics information of more wheel voice signals is extracted in framing, is generated according to voice characteristics information and acoustic model and takes turns voice more
The recognition result of signal；
According to recognition result and preset the mute detection algorithm Preliminary detection sound ends for taking turns voice signals out more；
Calculate the confidence information of more wheel voice signals；
The semantic information of the more wheel voice signals of parsing；
The corresponding speech analysis result of more wheel voice signals is obtained according to confidence information and semantic information；
The trip planning of the user is identified according to speech analysis result.
5. route planning method according to claim 4, which is characterized in that described according to voice characteristics information and acoustic mode
Type generates the recognition result of more wheel voice signals, specifically includes:
After generating voice characteristics information, take turns voice signal in each modeling unit using every frame is calculated based on acoustic model more
Likelihood value, optimum state metastasis sequence and its corresponding word sequence are obtained by dynamic programming algorithm, and by it is obtained most
Excellent state metastasis sequence and its corresponding word sequence are as recognition result.
6. route planning method according to claim 5, which is characterized in that the acoustic model is neural network based
Acoustic model identifies the voice characteristics information using the acoustic model neural network based.
7. route planning method according to claim 6, which is characterized in that the confidence level of voice signal is taken turns in the calculating more
The step of information, specifically includes:
Take turns voice signal according to the signal-to-noise ratio computation of recognition result, the sound end of more wheel voice signals and more wheel voice signals more
Confidence information.
8. route planning method according to claim 7, which is characterized in that described according to recognition result, more wheel voice letters
Number sound end and the signal-to-noise ratio computations of more wheel voice signals take turns the confidence information of voice signal more, specifically include:
Based on recognition result, the acoustics posterior probability of each word between sound end is calculated, wherein calculate between sound end
The formula of acoustics posterior probability of k-th of word include:
Wherein P (X) is the acoustics posterior probability of k-th of word in more wheel voice signals, the word corresponding modeling when p (m | x) is t frame
The likelihood value of unit,When for t frame the likelihood value of all modeling units and, T (X) is the duration of the word；
According to the acoustics posterior probability of each word between sound end and the corresponding confidence information of each word of signal-to-noise ratio computation.
9. route planning method according to claim 8, which is characterized in that each word according between sound end
Acoustics posterior probability and the corresponding confidence information of each word of signal-to-noise ratio computation, specifically include:
Acoustics posterior probability and signal-to-noise ratio based on current word, calculate the confidence level CM (X) of current word, and formula includes:
CM (X)=w*P (X)+(1-w) * SNR (X)
Wherein 0≤w≤1, w are weight coefficient；
Calculate the formula for taking turns the corresponding confidence level of voice signals includes: more
Wherein, t (x) indicates that the duration of n-th of word is long, and CM (x) indicates the confidence level of n-th of word；
Read group total is carried out to the corresponding confidence level of each word, acquires the confidence information of more wheel voice signals.
10. a kind of intelligent navigation equipment, which is characterized in that the intelligent navigation equipment includes processor, and the processor is used for
Program data is executed, to realize -9 described in any item route planning methods based on more wheel interactive voices according to claim 1
The step of.
11. a kind of automatic driving vehicle, which is characterized in that be provided with processor and be used for when the processor executes program data
The step of realizing -9 described in any item route planning methods based on more wheel interactive voices according to claim 1.
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