Adapting Off-the-Shelf Speech Recognition Systems for Novel Words

نویسندگان

چکیده

Current speech recognition systems with fixed vocabularies have difficulties recognizing Out-of-Vocabulary words (OOVs) such as proper nouns and new words. This leads to misunderstandings or even failures in dialog systems. Ensuring effective is crucial for the functioning of robot assistants. Non-native accents, vocabulary, aging voices can cause malfunctions a system. If this task not executed correctly, assistant will inevitably produce false random responses. In paper, we used statistical approach based on distance algorithms improve OOV correction. We developed post-processing algorithm be combined model. sense, compared two algorithms: Damerau–Levenshtein Levenshtein distance. validated performance conjunction five off-the-shelf models. Damerau–Levenshtein, algorithm, succeeded minimizing Word Error Rate (WER) when using MoroccanFrench test set systems, namely VOSK API, Google Wav2vec2.0, SpeechBrain, Quartznet pre-trained Our method works regardless architecture recognizer, its results our outperformed chosen recognizer

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ژورنال

عنوان ژورنال: Information

سال: 2023

ISSN: ['2078-2489']

DOI: https://doi.org/10.3390/info14030179