The evaluation of SpeechVerse models focuses on their performance across various speech tasks, specifically Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU). Initial findings show that SpeechVerse achieves competitive word error rates on multiple ASR benchmark datasets compared to specialized models like Whisper. However, a decrease in performance for the multi-task model suggests that balancing different task weights during training can affect results. Ultimately, the framework shows promise in integrating speech and language functionalities despite the complexity of multi-task learning.
The SpeechVerse framework demonstrates effective end-to-end joint speech and language capabilities, outperforming some specialized models in specific tasks while balancing performance across diverse benchmarks.
Evaluating SpeechVerse on ASR and SLU tasks reveals that the framework achieves competitive performance compared to task-specific models, but with challenges in maintaining balance during multi-task training.
#speech-recognition #natural-language-processing #multi-task-learning #machine-learning #speech-models
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