Speech Endpointing Using Context Data
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Solution Overview
Problem
Traditional speech recognition systems face inaccuracies and increased latency due to incorrect endpointing, especially in noisy conditions, as they rely on pause duration to determine the start and end of utterances, leading to incomplete processing and user dissatisfaction.
Innovation Solution
The use of client-provided context data to improve speech endpointing by comparing intermediate speech recognition results with expected results, dynamically adjusting the end-of-speech timeout to ensure accurate and timely recognition, thereby reducing latency and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional endpointers use pause duration to determine utterance boundaries, then the system operation is simple, but speech recognition accuracy deteriorates due to incorrect endpointing
Solution Approach 1:
The system performs preliminary actions by obtaining context data before speech recognition and preparing expected speech recognition results in advance. This allows the endpointer to compare intermediate results against pre-prepared expectations, improving accuracy without adding complex real-time processing
Solution Approach 2:
The system implements feedback by continuously comparing intermediate speech recognition results with expected results derived from context data. This feedback loop enables dynamic adjustment of endpointing decisions, improving accuracy through iterative verification rather than relying solely on simple pause detection
2Productivity
If traditional endpointers segment voice input at long pauses, then processing speed is fast, but speech recognition accuracy deteriorates due to incomplete phrase processing
Solution Approach 1:
The system dynamically adjusts the endpointing decision by comparing intermediate recognition results against expected results. Rather than using a fixed pause-duration threshold, the endpointing point is dynamically determined based on whether the recognized phrase matches expected context, allowing fast processing without sacrificing completeness
Solution Approach 2:
The system changes the parameter used for endpointing from fixed pause duration to dynamic matching against expected speech recognition results. This parameter change allows the system to maintain fast processing speeds while improving accuracy by verifying that segmented phrases are complete and contextually appropriate
3Measurement precision
If the system waits for complete utterances in noisy conditions, then speech recognition accuracy improves, but latency increases due to extended waiting time
Solution Approach 1:
The system performs preliminary action by obtaining context data and preparing expected speech recognition results before the speech input is fully processed. This pre-preparation allows the system to quickly verify completeness against expectations rather than waiting for traditional timeout periods, reducing latency while maintaining accuracy in noisy conditions
Solution Approach 2:
The system uses feedback by comparing intermediate recognition results with expected results derived from context. This immediate feedback mechanism allows the system to confidently endpoint and return results faster, reducing latency while maintaining accuracy by verifying completeness against pre-established expectations rather than relying on extended waiting
4Measurement precision
If the system uses context data to verify speech recognition results, then speech recognition accuracy improves, but device complexity increases due to additional processing steps
Solution Approach 1:
The system performs preliminary action by obtaining context data and preparing expected speech recognition results before processing the actual speech input. This pre-computation of expectations simplifies the main processing step to a straightforward comparison, improving accuracy without proportionally increasing overall system complexity
Solution Approach 2:
The system changes the verification parameter from traditional confidence scores to matching against expected results derived from context data. This parameter change improves accuracy by using contextual expectations as the verification criterion, while the complexity increase is mitigated by the efficiency of string/match comparison operations
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving audio data including an utterance, obtaining context data that indicates one or more expected speech recognition results, determining an expected speech recognition result based on the context data, receiving an intermediate speech recognition result generated by a speech recognition engine, comparing the intermediate speech recognition result to the expected speech recognition result for the audio data based on the context data, determining whether the intermediate speech recognition result corresponds to the expected speech recognition result for the audio data based on the context data, and setting an end of speech condition and providing a final speech recognition result in response to determining the intermediate speech recognition result matches the expected speech recognition result, the final speech recognition result including the one or more expected speech recognition results indicated by the context data.