CN112863491A - Voice transcription method and device and electronic equipment - Google Patents
Voice transcription method and device and electronic equipment Download PDFInfo
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/04—Segmentation; Word boundary detection
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/28—Constructional details of speech recognition systems
- G10L15/30—Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
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- G10L15/34—Adaptation of a single recogniser for parallel processing, e.g. by use of multiple processors or cloud computing
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/78—Detection of presence or absence of voice signals
- G10L25/87—Detection of discrete points within a voice signal
Abstract
The present disclosure relates to a voice transcription method, and relates to the technical field of voice processing, wherein the voice transcription method comprises the following steps: obtaining audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment; identifying semantic segmentation points within the buffer; and performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment. The invention can avoid the error caused by the discontinuous semantics in the voice writing process and improve the accuracy of the voice transcription.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of voice processing, and more particularly, to a voice transcription method, a voice transcription device and electronic equipment.
Background
In consideration of the real-time property of audio data transmission and the safety of data, recording equipment generally needs to automatically cut a record and upload the record to the cloud end every certain time when recording audio. The current scheme includes uploading at a fixed time or automatically after the recording duration reaches a certain time.
However, the above method does not take into consideration the actual situation, and is a kind of violent cutting, which may result in the audio being spoken being cut off, and may affect the processing of the subsequent audio, for example, may bring text separation results to ASR transcription (Automatic Speech Recognition), which may affect semantic translation and semantic consistency.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a new technical solution for a voice transcription method, a device and an electronic device.
According to a first aspect of the present disclosure, there is provided a voice transcription method, including:
obtaining audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment;
identifying semantic segmentation points within the buffer;
and performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment.
Optionally, the buffer is audio data in a first time period after the audio cut point.
Optionally, identifying semantic segmentation points within the buffer comprises: and detecting whether a mute part exists in the buffer area by adopting a voice activity detection technology, and if so, determining that the mute part is a semantic segmentation point.
Optionally, the mute time at the mute is greater than a preset time period.
Optionally, performing voice transcription according to the first audio segment, the semantic segmentation point, and the second audio segment, including: and performing voice transcription on the audio data before the semantic division point in the first audio segment by taking the semantic division point as a center, and performing voice transcription on the audio data after the semantic division point in the second audio segment.
According to a second aspect of the present disclosure, there is also provided a voice transcription apparatus, the apparatus including:
a data acquisition module for acquiring audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment;
the identification module is used for identifying semantic segmentation points in the buffer area;
and the voice transcription module is used for performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment.
Optionally, the recognition module is further configured to detect whether a silence location exists in the buffer area by using a voice activity detection technology, and if so, determine that the silence location is a semantic segmentation point.
Optionally, the voice transcription module is further configured to perform voice transcription on the audio data in the first audio segment before the semantic division point with the semantic division point as a center, and perform voice transcription on the audio data in the second audio segment after the semantic division point.
According to a third aspect of the present disclosure, there is also provided an electronic device comprising a memory for storing a computer program and a processor; the processor is adapted to execute the computer program to implement the method according to any of the first aspects.
According to a fourth aspect of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to any one of the first aspects.
The voice transcription method has the advantages that the complete voice chain is stored in the mode that the buffer area is arranged at the voice cutting point, and then the voice detection method is used for detecting the mute position as the semantic cutting point, so that errors caused by discontinuous semantics in the voice writing process are avoided, and the accuracy of voice transcription is improved.
Other features of embodiments of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which is to be read in connection with the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the embodiments of the disclosure.
Fig. 1 is a flowchart of a voice transcription method provided in this embodiment;
FIG. 2 is a schematic diagram of an audio data segment structure according to one embodiment;
fig. 3 is a schematic structural diagram of a speech transcription apparatus provided in this embodiment.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Various embodiments and examples according to the present invention are described below with reference to the accompanying drawings.
Referring to fig. 1, the present embodiment provides a voice transcription method, including:
step S1: audio data comprising at least one audio cut point is obtained, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment.
In a feasible example, the recording device uploads the acquired audio data to the cloud, the cloud receives the audio data, and the recording device automatically cuts the audio data when recording the audio, so that the audio data comprises an audio cutting point, the cloud identifies the audio cutting point, an audio segment before the audio cutting point is a first audio segment, and an audio segment after the audio cutting point is a second audio segment.
In this embodiment, a buffer may be provided, so that the duration of the first audio segment has a delay, and the delay and the second audio segment have an overlapping segment, where the overlapping segment is the buffer, that is, the buffer is audio data in a first time period after the audio cut point.
Step S2: semantic segmentation points within the buffer are identified.
In the existing scheme, the brute force cutting does not consider the influence of direct cutting on semantics, and in this embodiment, a Voice Activity Detection technology (VAD) is used to detect whether a silence location exists in a buffer, and if so, the silence location is determined to be a semantic segmentation point.
Specifically, when the mute time at the mute place is greater than a preset time period, the place is determined to be a semantic segmentation point, and the preset time period may be 2 seconds.
Step S3: and performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment.
Specifically, with the semantic division point as the center, voice transcription is performed on the audio data before the semantic division point in the first audio segment, and voice transcription is performed on the audio data after the semantic division point in the second audio segment.
In the following, in a specific example, the scheme of the present embodiment is explained:
referring to fig. 2, in a first audio segment, audio of 10 o 'clock to 11 o' clock and zero 30 seconds is recorded, and in a second audio segment, audio of 11 o 'clock to 12 o' clock is recorded, wherein audio data of 11 o 'clock to 11 o' clock and zero 30 seconds is a buffer, and when the audio data of 11 o 'clock to 11 o' clock and zero 12 seconds is found by VAD technology, a mute segment of more than 2S appears, that is, a semantic division point is located here, therefore, when ASR is transcribed, a voice transcribed text part of the first audio segment takes audio of 10 o 'clock to 11 o' clock 12 seconds, and a first audio segment takes audio of 11 o 'clock and 12 seconds to 12 o' clock, and the transcribed content must be complete and continuous.
In the embodiment, the complete voice chain is stored by setting the buffer area at the voice cut point, and then the voice detection method is used for detecting the mute part as the semantic cut point, so that errors caused by discontinuous semantics in the voice writing process are avoided, and the accuracy of voice transcription is improved.
The present embodiment provides a voice transcription apparatus 20, referring to fig. 3, the apparatus including:
a data acquisition module 23 is arranged to acquire audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping said second audio segment. Setting a buffer zone to make the time length of the first audio frequency segment have a time delay, the time delay and the second audio frequency segment have an overlapping segment, the overlapping segment is the buffer zone, that is, the buffer zone is the audio data in the first time segment after the audio frequency cutting point.
And the identification module 22 is used for identifying semantic segmentation points in the buffer area. And detecting whether a mute part exists in the buffer area by adopting VAD (voice activity detection), and if so, determining that the mute part is a semantic segmentation point.
And the voice transcription module 21 is used for performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment. And taking the semantic division point as a center, performing voice transcription on the audio data before the semantic division point in the first audio segment, and performing voice transcription on the audio data after the semantic division point in the second audio segment.
In the embodiment, the complete voice chain is stored by setting the buffer area at the voice cut point, and then the voice detection method is used for detecting the mute part as the semantic cut point, so that errors caused by discontinuous semantics in the voice writing process are avoided, and the accuracy of voice transcription is improved.
An electronic device comprising a memory for storing a computer program and a processor; the processor is used for executing the computer program to realize the method of the above embodiment.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of the above-mentioned embodiments.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method of voice transcription, comprising:
obtaining audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment;
identifying semantic segmentation points within the buffer;
and performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment.
2. The method of claim 1, wherein the buffer is audio data in a first time period after an audio cut point.
3. The method of claim 1, wherein identifying semantic segmentation points within the buffer comprises:
and detecting whether a mute part exists in the buffer area by adopting a voice activity detection technology, and if so, determining that the mute part is a semantic segmentation point.
4. A voice transcription method as claimed in claim 3, characterized in that the mute time at the mute is greater than a preset time period.
5. The method of claim 1, wherein performing speech transcription based on the first audio segment, semantic segmentation points, and second audio segment comprises:
and performing voice transcription on the audio data before the semantic division point in the first audio segment by taking the semantic division point as a center, and performing voice transcription on the audio data after the semantic division point in the second audio segment.
6. An apparatus for voice transcription, the apparatus comprising:
a data acquisition module for acquiring audio data comprising at least one audio cut point, the audio data comprising at least a first audio segment and a second audio segment, the first audio segment comprising a buffer overlapping the second audio segment;
the identification module is used for identifying semantic segmentation points in the buffer area;
and the voice transcription module is used for performing voice transcription according to the first audio segment, the semantic segmentation point and the second audio segment.
7. The apparatus of claim 6, wherein the recognition module is further configured to detect whether there is a silence location in the buffer area by using a voice activity detection technique, and if so, determine that the silence location is a semantic segmentation point.
8. The apparatus of claim 6, wherein the speech transcription module is further configured to perform speech transcription on the audio data in the first audio segment before the semantic segmentation point and to perform speech transcription on the audio data in the second audio segment after the semantic segmentation point, centered on the semantic segmentation point.
9. An electronic device comprising a memory and a processor, the memory for storing a computer program; the processor is adapted to execute the computer program to implement the method according to any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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| CN202110264849.XA CN112863491A (en) | 2021-03-12 | 2021-03-12 | Voice transcription method and device and electronic equipment |
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| CN109065023A (en) * | 2018-08-23 | 2018-12-21 | 广州势必可赢网络科技有限公司 | A kind of voice identification method, device, equipment and computer readable storage medium |
| CN110310657A (en) * | 2019-07-10 | 2019-10-08 | 北京猎户星空科技有限公司 | A kind of audio data processing method and device |
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- 2021-03-12 CN CN202110264849.XA patent/CN112863491A/en active Pending
Patent Citations (4)
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| US20120323585A1 (en) * | 2011-06-14 | 2012-12-20 | Polycom, Inc. | Artifact Reduction in Time Compression |
| CN108182945A (en) * | 2018-03-12 | 2018-06-19 | 广州势必可赢网络科技有限公司 | A kind of more voice cents based on vocal print feature are from method and device |
| CN109065023A (en) * | 2018-08-23 | 2018-12-21 | 广州势必可赢网络科技有限公司 | A kind of voice identification method, device, equipment and computer readable storage medium |
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Application publication date: 20210528 |