Hotword Classifiers for Speech Locale Selection
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Solution Overview
Problem
Current speech recognition systems face inefficiencies in accurately recognizing speech across different languages and dialects, as they often require computationally intensive processing using multiple speech locale-specific models, which can be unnecessary if the language and dialect can be accurately identified through hotwords.
Innovation Solution
The system employs multiple speech locale-specific hotword classifiers to identify the most likely speech locale based on hotwords, allowing it to select the appropriate speech recognition model for processing, thereby reducing computational load by assuming the rest of the utterance follows the same locale as the detected hotword.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses multiple speech locale-specific speech recognition models to accurately recognize speech in different languages and dialects, then speech recognition accuracy is improved, but computational processing load increases
Solution Approach 1:
The system segments the speech processing task into two stages: first, a hotword classifier quickly identifies the speech locale by detecting locale-specific hotwords; second, only the selected locale-specific speech recognition model processes the full utterance. This segmentation avoids the need to run multiple computationally intensive speech recognition models simultaneously.
Solution Approach 2:
The system performs preliminary classification of the speech locale using hotword detection before committing to full speech recognition processing. By identifying the locale early through hotwords, the system prepares the appropriate speech recognition model in advance, avoiding the need to evaluate multiple models during the recognition phase.
2Adaptability or versatility
If the system performs speech recognition using multiple speech locale-specific models to handle user cases where locale is not manually selected or updated, then speech recognition coverage is improved, but processing time increases
Solution Approach 1:
The system separates locale identification from full speech recognition by using a lightweight hotword classifier first. This segmentation allows the system to quickly determine the speech locale without running multiple full speech recognition models, thereby maintaining versatility while reducing processing time.
Solution Approach 2:
The hotword classifier acts as an intermediary between the raw speech input and the speech recognition models. It provides a quick preliminary assessment of the speech locale, enabling the system to adapt to different languages and dialects without the time penalty of running multiple speech recognition models.
3Measurement precision
If the system uses locale-specific speech recognition models for accurate speech recognition, then speech recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the speech processing architecture into a simple hotword classification layer and a locale-specific speech recognition layer. This segmentation allows the system to maintain multiple locale-specific models for accuracy while using a simple hotword classifier to manage complexity by selecting which model to use.
Solution Approach 2:
The hotword classifier serves as an intermediary that simplifies the interaction between the user's speech and the multiple locale-specific speech recognition models. It reduces system complexity by providing a straightforward mechanism for locale identification without requiring complex model selection logic.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for recognizing speech in an utterance. The methods, systems, and apparatus include actions of receiving an utterance and obtaining acoustic features from the utterance. Further actions include providing the acoustic features from the utterance to multiple speech locale-specific hotword classifiers. Each speech locale-specific hotword classifier (i) may be associated with a respective speech locale, and (ii) may be configured to classify audio features as corresponding to, or as not corresponding to, a respective predefined term. Additional actions may include selecting a speech locale for use in transcribing the utterance based on one or more results from the multiple speech locale-specific hotword classifiers in response to providing the acoustic features from the utterance to the multiple speech locale-specific hotword classifiers. Further actions may include selecting parameters for automated speech recognition based on the selected speech locale.