Speech Recognition Device Context Vector Integration
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Solution Overview
Problem
Existing end-to-end speech recognition systems fail to effectively utilize context information across multiple utterances, leading to inaccurate speech recognition when handling long sequences of speech, as they independently process each utterance without considering relationships between them.
Innovation Solution
A speech recognition device that learns model parameters using time-series data of word and acoustic feature sequences, allowing it to recognize word sequences by incorporating previous recognized word sequences as context, thereby enhancing the likelihood estimation for improved speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If end-to-end speech recognition processes each utterance independently, then processing speed is improved, but recognition accuracy deteriorates due to loss of context information
Solution Approach 1:
The patent merges the processing of multiple utterances by introducing a context model that combines information from previous utterances with the current utterance. The context vector C_t is generated by integrating word sequence information from prior utterances W_{1:t-1} with the acoustic features of the current utterance X_t, allowing the system to maintain processing efficiency while improving recognition accuracy through context utilization.
2Measurement precision
If context information from previous utterances is incorporated, then recognition accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent introduces a context vector C_t as an intermediary that summarizes and transmits essential information from previous utterances to the current utterance processing. This context vector acts as a compressed representation that captures relevant contextual information without requiring the system to process all historical data, thereby reducing computational complexity while maintaining recognition accuracy.
Solution Approach 2:
The patent transforms the complex task of processing entire previous utterance sequences into a simplified parameter representation (context vector C_t). By changing the parameter form from full word sequences to condensed vector representations, the system reduces computational complexity while preserving the essential contextual information needed for accurate recognition.
3Loss of information
If the model processes long sequences of utterances, then context utilization is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by maintaining a running context vector C_t that is continuously updated as each utterance is processed. This allows the system to have context information ready in advance for the next utterance, eliminating the need for retrospective processing and reducing overall processing time while maintaining full context utilization.
Data Source
AI summary
Provided is a speech recognition device capable of implementing end-to-end speech recognition considering a context. The speech recognition device includes a model parameter learning unit that learns a model parameter θ by using a word sequence of concern as an observation value and using a word sequence previous to the word sequence of concern, an acoustic feature value sequence corresponding to the word sequence of concern, and the model parameter θ as parameters to perform maximum likelihood estimation for a likelihood function of a probability that the observation value occurs under the parameters and an uttered speech recognition unit that repeats, in order of time sequence, processing of recognizing a word sequence to be recognized, the processing of recognizing the word sequence to be recognized being performed by using the word sequence to be recognized as an observation value and using an already recognized word sequence previous to the word sequence to be recognized, an acoustic feature value sequence corresponding to the word sequence to be recognized, and the learned model parameter θ as parameters and based on a maximum likelihood criterion for the likelihood function of the probability that the observation value occurs under the parameters.