Hyperarticulation Detection via Pairwise Voice Query Comparison
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Solution Overview
Problem
Current approaches to hyperarticulation detection in speech recognition systems classify single utterances independently, leading to poor performance due to the lack of context from previous queries, making it difficult to distinguish between normal speaking style and hyperarticulation.
Innovation Solution
The system employs pairwise comparison of consecutive voice queries with lexical overlap to extract comparative features, creating a non-speaker-specific model that analyzes changes in articulation using features like frequency, duration, and loudness, and re-ranks hypotheses to improve speech recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single utterance classification is used for hyperarticulation detection, then the system is simple to implement, but the detection accuracy is poor due to lack of context
Solution Approach 1:
The patent transitions from analyzing single utterances in isolation to comparing utterances across multiple dimensions (temporal sequence, lexical overlap, acoustic features). By adding the dimension of pairwise comparison between consecutive utterances, the system gains contextual information that significantly improves hyperarticulation detection accuracy without requiring overly complex architecture.
Solution Approach 2:
The system performs preliminary processing by extracting acoustic features and determining lexical overlap between consecutive utterances before making the hyperarticulation detection decision. This preliminary analysis of contextual information from previous utterances enables more accurate detection while keeping the overall system structure manageable.
2Measurement precision
If pairwise comparison of consecutive utterances is used, then hyperarticulation detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant features for hyperarticulation detection from the audio data, such as acoustic features (frequency, duration, energy) and lexical overlap metrics. By selecting and extracting only the critical comparative features between consecutive utterances rather than processing all possible data, the system achieves high detection accuracy while reducing computational power requirements.
3Adaptability or versatility
If non-speaker-specific model is used, then the system is more adaptable to different users, but it requires more data processing to account for individual variations
Solution Approach 1:
The patent develops a universal hyperarticulation detection model that works across different speakers without requiring speaker-specific training data or complex adaptation mechanisms. The model uses general acoustic features and contextual comparison approaches that are applicable to all users, making the system highly adaptable while keeping the model structure relatively simple and avoiding the need for extensive per-speaker customization.
Data Source
AI summary
Automatic speech recognition systems can benefit from cues in user voice such as hyperarticulation. Traditional approaches typically attempt to define and detect an absolute state of hyperarticulation, which is very difficult, especially on short voice queries. This disclosure provides for an approach for hyperarticulation detection using pair-wise comparisons and on a real-world speech recognition system. The disclosed approach uses delta features extracted from a pair of repetitive user utterances. The improvements provided by the disclosed systems and methods include improvements in word error rate by using hyperarticulation information as a feature in a second pass N-best hypotheses rescoring setup.