Accent Invariant Speech Recognition via Feature Space Transformation
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Solution Overview
Problem
Existing speech recognition systems face significant degradation or failure when encountering various non-native speaking customers with significant accents, as they struggle to accurately recognize speech elements like words and phonemes.
Innovation Solution
The method involves maintaining a database of language units with audio samples from multiple speakers, extracting feature vectors, calculating transformations to reduce pronunciation variation distances, and training a processor to classify accents invariantly, using techniques like Linear Discriminant Analysis (LDA) and multiple classification engines for accent identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is trained on pre-recorded audio samples from a target population, then recognition accuracy for that population is improved, but recognition performance degrades for speakers with different accents
Solution Approach 1:
The audio samples are segmented into multiple feature vectors that capture different acoustic characteristics. Each feature vector represents a specific aspect of the pronunciation (e.g., formant frequencies, spectral features), allowing the system to analyze and compare individual acoustic properties rather than treating the entire signal as a single unit.
Solution Approach 2:
The system transforms the feature space by applying mathematical transformations (such as Linear Discriminant Analysis) that reparameterize the acoustic features. This transformation modifies the parameter space to maximize separation between different language units while minimizing variation due to accents, effectively changing the parameters from raw acoustic features to accent-invariant features.
2Speed
If the speech recognition system uses standard spectral analysis, then processing speed is maintained, but accuracy drops for non-native speakers with significant accents
Solution Approach 1:
The system performs preliminary transformation of the audio samples into feature vectors and calculates pronunciation variation distances before the actual recognition process. This preprocessing step creates a transformed feature space where accent variations are minimized, so that the subsequent recognition operation can proceed efficiently with high accuracy without requiring repeated transformations during processing.
3Adaptability or versatility
If multiple audio samples from different speakers are stored in the database, then the system covers more accent variations, but the database size and processing complexity increase
Solution Approach 1:
Instead of storing and processing all raw audio samples from multiple speakers, the system creates transformed copies in the form of feature vectors that represent the essential acoustic characteristics. These feature vector copies capture the pronunciation variations while being more compact and suitable for efficient comparison and classification operations.
Solution Approach 2:
The system transitions from the time-domain audio signals to a higher-dimensional feature space where each audio sample is represented by multiple feature vectors. This dimensional transformation allows the system to capture complex acoustic patterns in a structured space that facilitates efficient computation and comparison, reducing the effective complexity of handling diverse accent variations.
Data Source
AI summary
A system and method for accent invariant speech recognition comprising: maintaining a database scoring a set of language units in a given language, and for each of the language units, scoring audio samples of pronunciation variations of the language unit pronounced by a plurality of speakers; extracting and storing m the database a feature vector for locating each of the audio samples in a feature space; identifying pronunciation variation distances, which are distances between locations of audio samples of the same language unit in the feature space, and inter-unit distances, which are distances between locations of audio samples of different language units in the feature space; calculating a transformation applicable on the feature space to reduce the pronunciation variation distances relative to the inter-unit distances; and based on the calculated transformation, training a processor to classify as a same language unit pronunciation variations of the same language unit.