Unified Speech Recognition Endpointing With VAD-EOQ Switching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional speech recognition systems face challenges in achieving high-quality voice activity detection (VAD) and end-of-query (EOQ) detection, which are critical for reducing latency and computational burden, especially in battery-powered devices.
Innovation Solution
An end-to-end multitask model integrates a speech recognition model with an endpointer model, sharing layers and being jointly trained to perform both tasks, with a switch connection allowing the endpointer model to operate in VAD and EOQ detection modes, leveraging latent representations generated by the audio encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are used for speech recognition and endpointing, then each model can be optimized independently, but the system complexity increases and latency increases
Solution Approach 1:
The patent combines speech recognition and endpointing into a single unified model that performs both functions simultaneously. The model integrates an audio encoder, decoder, and endpointer components that work together to achieve both speech recognition and endpoint detection without requiring separate independent models, thereby reducing system complexity while maintaining functionality.
Solution Approach 2:
The unified model serves multiple purposes: it performs speech recognition to transcribe spoken input and simultaneously performs endpointing to detect voice activity and end-of-query points. This multi-functional approach allows a single model to replace what would traditionally require separate specialized models, reducing overall system complexity.
2Measurement precision
If separate models are used for speech recognition and endpointing, then each model can be optimized independently, but the computational burden increases
Solution Approach 1:
By merging speech recognition and endpointing into one unified model, the patent eliminates the need to run two separate models, thereby reducing the total computational burden. The shared audio encoder and coordinated processing reduce redundant computations while maintaining the quality of both speech recognition and endpointing functions.
3Ease of operation
If traditional endpointing methods are used, then implementation is simpler, but latency increases and detection precision decreases
Solution Approach 1:
The endpointer component continuously monitors audio input and prepares endpoint decisions in real-time during the speech recognition process. This preliminary action allows the system to detect voice activity and end-of-query points without adding significant processing delay, as the endpointing analysis occurs concurrently with speech encoding rather than as a separate post-processing step.
4Ease of operation
If traditional endpointing methods are used, then implementation is simpler, but detection precision decreases
Solution Approach 1:
The unified model uses the audio encoder's latent representations as an intermediary to improve endpointing accuracy. Instead of applying traditional endpointing methods directly to raw audio, the system leverages the encoded audio features and attention mechanisms to make more accurate endpoint decisions, achieving higher precision while maintaining implementation feasibility through the integrated architecture.
Data Source
AI summary
A single E2E multitask model includes a speech recognition model and an endpointer model. The speech recognition model includes an audio encoder configured to encode a sequence of audio frames into corresponding higher-order feature representations, and a decoder configured to generate probability distributions over possible speech recognition hypotheses for the sequence of audio frames based on the higher-order feature representations. The endpointer model is configured to operate between a VAD mode and an EOQ detection mode. During the VAD mode, the endpointer model receives input audio frames, and determines, for each input audio 10 frame, whether the input audio frame includes speech. During the EOQ detection mode, the endpointer model receives latent representations for the sequence of audio frames output from the audio encoder, and determines, for each of the latent representation, whether the latent representation includes final silence.