Contributions to the contextualization of human-machine spoken interaction systems

Cuesta, Juan Manuel Lucas

doi:10.20868/upm.thesis.14934

Cited by 1 publication

(1 citation statement)

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“…These technological advancements have expanded the applications of speech recognition. With the advancement of technology and the growing need for human-computer interaction [7], speech recognition has found widespread use across various fields. Domain-specific speech recognition, for instance, converts speech signals into text or commands, facilitating natural, efficient, and convenient human-computer interaction [8].…”

Section: Introductionmentioning

confidence: 99%

A Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks

Dong

Ding

Zhai

et al. 2023

Sensors

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This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.

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Section: Introductionmentioning

confidence: 99%