Accent Detection via Acoustic Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional speech recognition systems face challenges in accurately interpreting speakers with voice characteristics not adequately represented in their training data, leading to poor performance for speakers with different accents or speech variations.
Innovation Solution
The system employs automatic accent detection by selecting appropriate acoustic models based on similarity analysis of user inputs, using a method that involves receiving utterance data, identifying outlier data, filtering, clustering, and selecting the most suitable acoustic models for accurate interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single acoustic model is trained on finite training data, then the system is simple and fast, but the accuracy for speakers with accents not represented in training data deteriorates
Solution Approach 1:
The patent segments the single acoustic model into multiple accent-specific acoustic models. Each model is trained on data from speakers with specific accent characteristics. When processing new speech, the system determines the speaker's accent and selects the corresponding specialized model, enabling high accuracy for diverse speakers while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system dynamically adapts by selecting different acoustic models based on the detected accent of the speaker. Instead of using a fixed single model, the system switches between multiple pre-trained accent-specific models depending on the input speech characteristics, enabling the system to adapt to different accents without requiring retraining
2Adaptability or versatility
If multiple acoustic models are used to cover different accents, then the adaptability to different accents improves, but the device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training multiple accent-specific acoustic models before actual use. Each model is prepared in advance with specialized training data for specific accent groups. During operation, the system only needs to select the appropriate pre-trained model based on accent detection, avoiding the complexity of training multiple models dynamically
Solution Approach 2:
The patent introduces an intermediary accent detection module that acts as a mediator between the input speech and the acoustic models. This intermediary component analyzes the speech, determines the speaker's accent, and selects the appropriate acoustic model, simplifying the overall system architecture by centralizing the selection logic rather than requiring complex model switching mechanisms
3Measurement precision
If conventional speech recognition systems use standard training data, then the training process is simple, but the measurement precision for speakers with different accents deteriorates
Solution Approach 1:
The patent applies local quality by training separate acoustic models for different local accent groups. Instead of using uniform training data for all speakers, the system creates specialized training datasets for specific accent regions (e.g., regional American English accents, international accents). Each model receives tailored training data relevant to its specific accent group, significantly improving measurement precision for speakers from different backgrounds
Data Source
AI summary
Systems and processes for automatic accent detection are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input, determining a first similarity between a representation of the user input and a first acoustic model of a plurality of acoustic models, and determining a second similarity between the representation of the user input and a second acoustic model of the plurality of acoustic models. The method further includes determining whether the first similarity is greater than the second similarity. In accordance with a determination that the first similarity is greater than the second similarity, the first acoustic model may be selected; and in accordance with a determination that the first similarity is not greater than the second similarity, the second acoustic model may be selected.