End of Speech Detection Using Neural Acoustic Models
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Solution Overview
Problem
Detecting the end of speech in audio signals is challenging, particularly in devices with limited resources, due to the lack of alignment data in audio clips and transcripts, which complicates training deep neural network-based speech recognition models.
Innovation Solution
The implementation of a speech detection pipeline that uses a neural acoustic model combined with connectionist temporal classification (CTC) to generate probability distributions for characters, and an end of speech (EOS) detector that integrates with the CTC module to detect the beginning and end of speech segments using a sliding window and threshold approach, without requiring additional components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network-based speech recognition models are used to detect end of speech, then detection accuracy is improved, but computational resources and device complexity increase
Solution Approach 1:
The speech detection pipeline is segmented into distinct functional modules: audio capture, neural acoustic model processing, CTC probability distribution generation, EOS detector with sliding window analysis, and speech segment identification. This modular segmentation allows the system to achieve high detection accuracy through specialized processing at each stage while managing device complexity by organizing functions into separable, independently optimizable components.
Solution Approach 2:
The system employs dynamic threshold adjustment and adaptive sliding window parameters that can be tuned based on acoustic conditions. The EOS detector dynamically adjusts its detection criteria using probability thresholds derived from the CTC model, allowing the system to maintain high accuracy across varying noise levels and speech characteristics without requiring a fixed complex architecture.
2Measurement precision
If deep neural network-based speech recognition models are used to detect end of speech, then detection accuracy is improved, but processing time and latency increase
Solution Approach 1:
The neural acoustic model and CTC module perform preliminary processing of the audio signal to generate probability distributions for all characters including blank characters before EOS detection. This preliminary action prepares the data in advance, allowing the EOS detector to quickly identify speech segments using simple threshold comparisons on pre-computed probabilities, thereby reducing overall processing latency while maintaining high accuracy.
Solution Approach 2:
The system uses efficient sliding window techniques that skip through the probability distribution sequence, rapidly identifying regions where blank character probabilities exceed thresholds. This approach rushes through the detection process by focusing computational effort only on relevant segments rather than uniformly processing entire audio streams, significantly reducing processing time.
3Adaptability or versatility
If resource-constrained devices are used for speech detection, then device portability and accessibility are improved, but detection reliability deteriorates
Solution Approach 1:
The system changes key parameters such as sliding window size, probability thresholds, and CTC beam width to optimize performance for resource-constrained devices. By adjusting these parameters, the system maintains reliable detection accuracy on portable devices with limited computational resources, achieving a balance between device portability and detection reliability through parameter optimization rather than architectural compromise.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to recognize speech in an audio signal. In particular, various embodiments can indicate an end of one or more speech segments based, at least in part, on one or more characters predicted to be within these one or more speech segments.