CN100431003C - Voice decoding method based on mixed network - Google Patents
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Abstract
The present invention particularly relates to a voice decoding method based on a mixed network, which belongs to the speech recognition field. The present invention comprises the steps that 1) the depth-first search of frame synchronization Viterbi-Beam is carried out for phonetic characteristics; N-Best sentences or word formats are output; 2) according to the time semblance algorithm and the phoneme semblance algorithm, the two-stage clustering of the N-Best sentences or the word formats is carried out; a confusion network is generated; 3) the confusion network uses the maximum of rear probability as a criterion, and the optimum result is searched. Compared with the existing multipass decoding method, when the present invention carries out the second decoding, fine and complex acoustic models and language models are not needed; the network is efficiently reduced, and the decoding speed rate is increased. Simultaneously, the present invention also overcomes the shortage that decoding errors can not be repaired in a multipass decoding system.
Description
Technical field
The invention belongs to field of speech recognition, specifically, relate to a kind of tone decoding method based on confusion network.
Background technology
Decode procedure, just usually said identifying is the important component part of speech recognition system.Its function is: under the condition of given acoustic model and language model, for the acoustic feature vector sequence of input, hunt out optimum coupling speech string automatically from certain search volume, voice signal has converted Word message to the most at last.
Fig. 1 is a kind of known speech recognition system structural drawing.As shown in the figure, characteristic extracting module is carried out the processing of branch frame to input speech signal, and frame length is 20ms usually, and frame moves and is 10ms; Feature commonly used has MFCC feature, LPC feature and PLP feature.After feature extraction, voice signal has converted feature vector sequence to.Utilize acoustic model and language model, decoder module carries out match search to the search volume that feature vector sequence constitutes, and obtains recognition result.Searching algorithm commonly used has: the frame synchronization Viterbi-Beam searching algorithm of depth-first and the A* searching algorithm of breadth-first.The used acoustic model of decoder module add up after to pronunciation unit modeling and is obtained, and it has described the physical characteristics of pronouncing.The three-tone model is an acoustic model commonly used at present.Phoneme is the basic comprising unit of pronunciation.And three-tone (TRIPHONE) is a kind of context-sensitive phoneme.Compare with single phoneme (single-tone), it can describe the not variation of the pronunciation of phoneme simultaneously situation of context.Language model is to add up to obtain from the corpus that contains a large amount of texts, has embodied the statistical property of language.The N grammatical model of unit (N=2 or 3) is present the most frequently used language model.
In recognition system shown in Figure 1, employing be one time the decoding (One-Pass).In order further to improve the recognition performance of speech recognition system, some systems adopt the recognition strategy of multipass decoding (Multi-Pass).Known speech recognition multipass decode system structural drawing as shown in Figure 2, its basic thought is: at first get rid of least possible situation with better simply information, dwindle the search volume; Progressively utilize complicated information to carry out precise search then.The decoding in back utilizes more information source (for example meticulousr acoustic model and language model) and more accurate search strategy last all on the decoded search volume, obtains more excellent recognition result.If in decode system, all information sources are all joined in the decode procedure, so huge search volume and calculated amount will make computing machine can't bear this search mission.The multipass decoding policy has promptly made full use of multiple information source, makes practical operation feasible again.
The intermediate result of so-called multipass decoding refers to last output all over decoding, and it also is the input of a back decoding simultaneously.This intermediate result has constituted the search volume of a back decoding.Intermediate result generally can be divided into by type: the 1) sentence that top n probability score is the highest (N-Best lists); 2) speech lattice (Word Lattice).The speech lattice are a kind of digraphs, are also referred to as speech figure.Node in the speech lattice is possible speech, the line between the annexation configuration node between speech.In fact, N-Best sentence itself also produces from the speech lattice.Relative N-Best sentence, the speech lattice are more effective for organizing of information, and big by the search volume that the speech lattice generate, the potential sentence number that comprises is more.
The method of another similar multipass decoding is ROVER.The recognition result of a plurality of recognition systems of this method synthesis in a kind of mode of voting, selects final result.Though the ROVER method does not need proper multipass decode procedure, setting up a plurality of recognition systems neither the simple thing that is easy to.
Present existing multipass decoded speech recognition system is because adopted complicated more and meticulous acoustic model and language model, so improved recognition performance in search procedure.But then, obtaining complicated meticulous model itself is not an easy thing.In addition, what present existing multipass decode system adopted is cascade structure, and this makes last all over can not get correction in the mistake decoding afterwards that occurs in the decoding forever.Each has all determined the annexation between speech and the speech all over after decoding.Because the decoding of back is to carry out on the last search volume that generates all over decoding, so mutual connectionless speech can not form annexation forever again.Yet the no connection state between some speech, may be since in the Viterbi search because Beam width size, or the description of acoustic model and language model is not accurate enough causes.Simultaneously, because Viterbi-Beam search self, what comprised some among the result in the middle of it only is to put the beginning and ending time slightly different and the identical speech of content.And the text message that just identifies that speech recognition system is concerned about, temporal information is not among considering.The existence of such speech, the processing of promising back does not increase quantity of information, but the search volume has been strengthened, and has promptly increased the search burden to a certain extent.
Summary of the invention
The objective of the invention is: overcome the deficiencies in the prior art, in the later stage of multipass decoding, under the situation of not utilizing more information, (promptly do not utilize the more acoustic model and the language model of elaborate), reduce the decoding error rate by the confusion network clustering technique, improve decode rate, thereby a kind of tone decoding method based on confusion network is provided.
To achieve these goals, the invention provides a kind of coding/decoding method, comprise step based on confusion network:
1) input speech signal is extracted feature, obtain feature vector sequence, utilize acoustic model and language model, phonetic feature is carried out depth-first frame synchronization Viterbi-Beam search, output N-Best sentence or speech lattice;
It is characterized in that, also comprise the steps:
2) N-Best sentence or speech lattice are carried out two-stage cluster generation confusion network according to time similarity algorithm and phoneme similarity algorithm;
3) on this confusion network, be criteria match to the maximum and search out optimal result with posterior probability.
Described step 2) generate the process of confusion network in, comprise following substep:
21) set up the initial class of obscuring according to beginning and ending time information, wherein not only speech is number identical but also the beginning and ending time is also identical for each speech of obscuring the class correspondence;
22) write down each and initially obscure annexation between class;
23) to there not being annexation and speech number identical class to carry out the time similarity cluster;
24) to not having annexation and having overlapping class to carry out phoneme similarity cluster on the time period;
25) travel through all classes of obscuring, calculate the posterior probability of each speech, obtain final confusion network.
The principle of carrying out the time similarity cluster described step 23) is: travel through all classes of obscuring, find out with that of current class time similarity maximum and obscure class, and merge into one with current class and new obscure class.
The principle of carrying out phoneme similarity cluster described step 24) is: travel through all classes of obscuring, find out with that of current class phoneme similarity maximum and obscure class, and merge into one with current class and new obscure class.
Described step 25) the forward-backward algorithm algorithm is adopted in the calculating of posterior probability in.
Described step 25) in, less than 1 the class of obscuring, is that it increases by one " omission speech ", makes that the posterior probability sum of all speech is 1 in each class for the posterior probability sum of all speech in this class.
Described step 2) in, at first the N-Best sentence in the step 1) is compressed into the directed networks structure by merge algorithm, and then generates confusion network according to the directed networks that obtains.
Compare with existing multipass coding/decoding method, the present invention does not need the more acoustic model and the language model of elaborate, the consumption of having saved operation time and memory headroom when second time decoding.Also alleviated simultaneously the task amount of model training.According to time similarity and phoneme similarity, from the intermediate result of first pass decoding, generated confusion network among the present invention.Owing to the not accurate enough speech that does not have annexation that causes of first pass decoding, might in confusion network, recover its annexation for those.This has just overcome the shortcoming that the decoding error can't be repaired in the existing multipass decode system.The present invention has carried out cluster to the speech that satisfies the time similarity condition in the process that generates confusion network.Because the characteristic that the Viterbi-Beam search is intrinsic exists a lot of beginning and ending times to have any different and the identical speech of content slightly in the intermediate result that first pass search back produces.After having carried out the time similarity cluster, these speech have just all synthesized a class.So just effectively reduce network, improved decode rate.
Description of drawings
Fig. 1 is known speech recognition system structural drawing;
Fig. 2 is known speech recognition multipass decode system structural drawing;
Fig. 3 is the tone decoding method process flow diagram based on confusion network provided by the invention;
Fig. 4 is compressed into the process flow diagram of network structure for NBest sentence among the present invention;
Fig. 5 is the general networking synoptic diagram;
Fig. 6 obscures the class synoptic diagram for initial among the present invention;
Fig. 7 is the confusion network synoptic diagram after the time similarity cluster among the present invention;
Fig. 8 is the confusion network synoptic diagram after the phoneme similarity cluster among the present invention;
Fig. 9 is final confusion network synoptic diagram among the present invention.
Embodiment
The present invention will be further described below in conjunction with accompanying drawing and preferred embodiment.
As shown in Figure 3, the tone decoding method based on confusion network provided by the invention comprises the steps:
Step 101: from input speech signal, extract feature vector sequence.
Step 102: with the Viterbi-Beam searching algorithm phonetic feature is carried out the decoding first time, export N-Best sentence or speech lattice, draw the acoustic layer probability score and the linguistic level probability score of each speech in N-Best sentence or the speech lattice simultaneously.
Step 103: if the intermediate result of output is the NBest sentence in the step 102, then it is compressed into the directed networks structure with merge algorithm, the flow process of this merge algorithm as shown in Figure 4, therefore it is a kind of prior art, no longer describes in detail here.If the intermediate result of output is the speech lattice in the step 102,, therefore directly enter step 104 because speech lattice itself also can be regarded a kind of network as.
Step 104: front directed networks structure is represented with specific data structure.That is: each speech is saved as a Node node, and the information that this node comprises has: this node sequence number, the speech of the speech of this node correspondence in dictionary number, the beginning and ending time of the speech of this node correspondence, the node ID of all subsequent node adjacent with this node.
Annexation between adjacent node is saved as a Link arc, the information that this arc comprises has: the sequence number of this arc, the start-stop Node node ID of this arc correspondence, the speech of the speech of this arc correspondence in dictionary number (number identical) with the corresponding speech of this arc terminal node, the acoustic layer probability score of the speech of this arc correspondence and linguistic level probability score.
Just can express whole directed networks with above-mentioned Node node and Link arc.
Step 105: utilize the acoustic layer probability score and the linguistic level probability score of speech on the Link arc, on the network that forms in step 104, the posterior probability with the speech on every arc of forward-backward algorithm algorithm computation is stored in result of calculation on the corresponding arc.The posterior probability of the speech on every arc is that probability sum by all paths of this arc is than all path probability sums in last this network.Formula is as follows:
Wherein: X represents the phonetic feature sequence; W is to be t the beginning and ending time
s, t
eSpeech; W
sWW
eForm complete path W in the network; W
sBe all possible preceding continuous speech string of w, and W
eBe all possible follow-up speech string of w.The physical meaning of following formula is: under the prerequisite of known features sequence X, at [t
s, t
e] time period produces the probability of speech w.
Therefore the forward-backward algorithm algorithm no longer describes in detail herein for well known to a person skilled in the art canonical algorithm.
Step 106: travel through all Link arcs, set up the initial class (ConfusionCluster) of obscuring according to beginning and ending time information.It is exactly the class of being made up of the speech that is not easy in the decode procedure to distinguish that what is called is obscured class.The speech of obscuring in the class is called confusable word.The mutual distinctive of confusable word is relatively poor, disturbs each other in decode procedure.To obscure the network that class forms be confusion network by such.Obscuring class is a set of Link arc.The information that it comprised has: the sequence number of contained arc, the speech of contained speech number (only write down unique speech number), minimax beginning and ending time.Initially obscuring class is made up of such some arcs: not only speech is number identical but also the beginning and ending time is also identical for the speech of their correspondences.At initial obscuring in the class, each class only contains a speech number.
According to the beginning and ending time order, all classes of initially obscuring are sorted.Write down each and initially obscure annexation between class, be saved among the two-dimensional array order.That is: if obscure class C
iThe termination Node node of certain Link arc of (promptly obscuring class for i) is for obscuring class C
jIn the initial Node node of certain Link arc, then C
iWith C
jBe the order[i that links to each other] [j]=1.If C
iWith C
jBetween annexation is arranged, and C
jWith C
kLink to each other, then think C
iWith C
kLink to each other, only they are not directly to link to each other yet.
Not having the class of initially obscuring of annexation is potential object that can cluster.
Step 107: carry out first order cluster, promptly speech number identical class is carried out the time similarity cluster.
Obscure class for each, finding out does not have annexation and corresponding speech number identical all to obscure class with it.Calculate these and obscure class and originally obscure time similarity between class, that of similarity maximum obscured class merge into one and new obscure class, and record is newly obscured class and other and obscured annexation between class with originally obscuring class.Travel through all classes of obscuring, finish top process.Each was obscured class and still only comprised a speech number this moment.
Wherein the time similarity computing formula is:
l
1, l
2Be respectively to obscure class C
i, C
jIn arc, overlap (l
1, l
2) be arc l
1With l
2The overlapping degree of the beginning and ending time of corresponding speech.
Step 108: carry out second level cluster, i.e. phoneme similarity cluster.
Travel through all classes of obscuring, obscure class for each, finding out does not have annexation with it and has overlapping all to obscure class on the time period.Calculate this and obscure class and the corresponding phoneme similarity of speech between class obscured that is found, this class of obscuring of obscuring class and similarity maximum is merged, generate one and new obscure class, and class and other newly obscured in record, and all obscure annexation between class.Process above repeating, until do not have to merge obscure class till.The speech of obscuring class and being comprised this moment number may more than one.
Wherein the phoneme similarity between two speech is:
W
1, W
2Be respectively and obscure class C
i, C
jIn the speech that comprises, number in dictionary, search by speech and to obtain.Sim (W
1, W
2) be speech W
1With W
2The number of identical phoneme in the corresponding phone string.
Step 109: travel through all classes of obscuring, calculate the posterior probability of each speech.The posterior probability of speech comprises the posterior probability sum of the arc of this speech number for all.If certain obscures the posterior probability sum of all speech in the class less than 1, in order to keep the unitarity integrality on the probability, for it increases by one " omission speech " (ellipsis), so that the posterior probability sum of all speech is 1 in each class." omission speech " means that this obscures class and might be skipped over.The network of this moment is final confusion network.
On final confusion network, carry out the maximum a posteriori probability search.That is: travel through all classes of obscuring, select the wherein recognition result of those speech composition outputs of posterior probability maximum, as shown in Figure 9.If the speech of picking out is " an omission speech ", show that then this other significant speech of obscuring in the class does not have enough competitive power, can not appear in the recognition result.
It more than is the detailed step of the tone decoding method based on confusion network provided by the invention.
The present invention at first utilizes three-tone acoustic model and three gram language model, and phonetic feature is carried out depth-first frame synchronization Viterbi-Beam search, output N-Best sentence or speech lattice.Different with multipass coding/decoding method commonly used is that this method is not on the search volume that last time, decoding obtained, with more the acoustic model and the language model of elaborate are decoded once more.This method does not need more model, but plans again by the search volume that last time, decoded result formed, and utilizes time similarity algorithm and phoneme similarity algorithm to carry out cluster, generates confusion network.On confusion network, search out recognition result at last with maximum a posteriori probability.The present invention has made full use of the information in the decoded result last time, effectively raises system performance.Simultaneously, the present invention has alleviated the task amount of model training.So-called training is exactly the process that obtains model parameter with the method for statistics.Train meticulousr model, just need more training data and more complicated training tool.According to time similarity and phoneme similarity, from the intermediate result of first pass decoding, generated confusion network among the present invention.Owing to the not accurate enough speech that does not have annexation that causes of first pass decoding, might in confusion network, recover its annexation for those.This has just overcome the shortcoming that the decoding error can't be repaired in the existing multipass decode system.The present invention has carried out cluster to the speech that satisfies the time similarity condition in the process that generates confusion network.Because the characteristic that the Viterbi-Beam search is intrinsic exists a lot of beginning and ending times to have any different and the identical speech of content slightly in the intermediate result that first pass search back produces.After having carried out the time similarity cluster, these speech have just all synthesized a class.So just effectively reduce network, improved decode rate.
Owing to do not need more accurate information (or model), the present invention can finish search procedure in real time second time.In the experiment, by after the confusion network maximum a posteriori search, the identification error rate of system can drop to 18.6% from 19.8%, and error rate has definitely descended 1.2%, has descended 6.0% relatively.
Claims (6)
1, a kind of tone decoding method based on confusion network comprises step:
1) input speech signal is extracted feature, obtain feature vector sequence, utilize acoustic model and language model, phonetic feature is carried out depth-first frame synchronization Viterbi-Beam search, output N-Best sentence or speech lattice;
It is characterized in that, also comprise the steps:
2) N-Best sentence or speech lattice are carried out two-stage cluster generation confusion network according to time similarity algorithm and phoneme similarity algorithm;
The process of wherein said generation confusion network comprises following substep:
21) set up the initial class of obscuring according to beginning and ending time information, wherein not only speech is number identical but also the beginning and ending time is also identical for each speech of obscuring the class correspondence;
22) write down each and initially obscure annexation between class;
23) to there not being annexation and speech number identical class to carry out the time similarity cluster;
24) to not having annexation and having overlapping class to carry out phoneme similarity cluster on the time period;
25) travel through all classes of obscuring, calculate the posterior probability of each speech, obtain final confusion network;
3) on this confusion network, be criteria match to the maximum and search out optimal result with posterior probability.
2, by the described tone decoding method of claim 1 based on confusion network, it is characterized in that, the principle of carrying out the time similarity cluster described step 23) is: travel through all classes of obscuring, find out with that of current class time similarity maximum and obscure class, and merge into one with current class and new obscure class.
3, by the described tone decoding method of claim 1 based on confusion network, it is characterized in that, the principle of carrying out phoneme similarity cluster described step 24) is: travel through all classes of obscuring, find out with that of current class phoneme similarity maximum and obscure class, and merge into one with current class and new obscure class.
4,, it is characterized in that described step 25 by the described tone decoding method of claim 1 based on confusion network) in the calculating of posterior probability adopt the forward-backward algorithm algorithm.
5, by the described tone decoding method of claim 1 based on confusion network, it is characterized in that described step 25) in, for the posterior probability sum of all speech in this class less than 1 the class of obscuring, be that it increases by one " omission speech ", make that the posterior probability sum of all speech is 1 in each class.
6, by the described tone decoding method of claim 1 based on confusion network, it is characterized in that, described step 2) in, at first the N-Best sentence in the step 1) is compressed into the directed networks structure by merge algorithm, and then generates confusion network according to the directed networks that obtains.
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| EP1975923B1 (en) * | 2007-03-28 | 2016-04-27 | Nuance Communications, Inc. | Multilingual non-native speech recognition |
| CN101996631B (en) | 2009-08-28 | 2014-12-03 | 国际商业机器公司 | Method and device for aligning texts |
| CN101887725A (en) * | 2010-04-30 | 2010-11-17 | 中国科学院声学研究所 | Phoneme confusion network-based phoneme posterior probability calculation method |
| US8972253B2 (en) * | 2010-09-15 | 2015-03-03 | Microsoft Technology Licensing, Llc | Deep belief network for large vocabulary continuous speech recognition |
| CN102063900A (en) * | 2010-11-26 | 2011-05-18 | 北京交通大学 | Speech recognition method and system for overcoming confusing pronunciation |
| CN102376305B (en) * | 2011-11-29 | 2013-06-19 | 安徽科大讯飞信息科技股份有限公司 | Speech recognition method and system |
| CN103730115B (en) * | 2013-12-27 | 2016-09-07 | 北京捷成世纪科技股份有限公司 | A kind of method and apparatus detecting keyword in voice |
| CN110197657B (en) * | 2019-05-22 | 2022-03-11 | 大连海事大学 | Dynamic sound feature extraction method based on cosine similarity |
| CN110992943B (en) * | 2019-12-23 | 2022-05-24 | 思必驰科技股份有限公司 | Semantic understanding method and system based on word confusion network |
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