Hidden Markov Model for Non-Language Speech Sound Detection
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Solution Overview
Problem
Current speech processing technologies face challenges in distinguishing non-language speech sounds (NLSS) from language speech sounds (LSS) due to similar acoustic characteristics, leading to errors in speech endpoint detection and other applications, especially when dealing with diverse input conditions such as speaker, channel, and noise variations.
Innovation Solution
A computerized method using a hidden Markov model (HMM) that isolates segments from an audio signal, extracts features like mel-frequency cepstral coefficients (MFCCs), pitch confidence, and cepstral variance, and classifies them as either LSS or NLSS, employing discriminative learning and adaptive model training to improve robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech processing approaches are used to distinguish background noise from speaker sounds, then the basic speech detection function is maintained, but non-language speech sounds (NLSS) are not properly detected, leading to errors in speech endpoint detection and other applications
Solution Approach 1:
The audio signal is segmented into multiple frames, and each frame is independently processed through feature extraction and HMM classification. This segmentation allows the system to detect NLSS at the frame level, improving overall detection accuracy while maintaining computational efficiency.
Solution Approach 2:
Hidden Markov Models serve as an intermediary between the raw audio signal and the classification output. The HMMs process acoustic features through a probabilistic model that mediates between simple acoustic analysis and complex linguistic interpretation, enabling robust NLSS detection without requiring high-level linguistic knowledge.
2Measurement precision
If higher-level linguistic analysis approaches are used for NLSS detection, then language-specific accuracy may improve, but robustness to speaker, channel, and noise conditions deteriorates
Solution Approach 1:
The system changes the detection parameters from language-specific linguistic features to acoustic features (MFCCs, pitch confidence, cepstral stationarity, cepstral variance) that are invariant across languages. This parameter transformation enables the same HMM model to detect NLSS robustly across different languages, speakers, and acoustic conditions without requiring language-specific training.
3Reliability
If acoustic features similar to phonemes are used for NLSS detection, then detection sensitivity increases, but false positive rates increase due to similar characteristics between NLSS and LSS
Solution Approach 1:
The system uses dynamic features including pitch confidence, cepstral stationarity, and cepstral variance that capture temporal and spectral changes in the audio signal. These dynamic characteristics help distinguish NLSS from LSS by detecting patterns of change rather than relying on static acoustic similarities, thereby reducing false positives while maintaining sensitivity.
Data Source
AI summary
A method, system and apparatus are shown for identifying non-language speech sounds in a speech or audio signal. An audio signal is segmented and feature vectors are extracted from the segments of the audio signal. The segment is classified using a hidden Markov model (HMM) that has been trained on sequences of these feature vectors. Post-processing components can be utilized to enhance classification. An embodiment is described in which the hidden Markov model is used to classify a segment as a language speech sound or one of a variety of non-language speech sounds. Another embodiment is described in which the hidden Markov model is trained using discriminative learning.