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== Impact on the Field == Hidden Markov Models (HMMs) have left an indelible mark on the field of speech recognition, shaping its evolution and expanding its horizons. Their impact is felt across various dimensions of speech processing, underscoring their enduring relevance and adaptability. * HMMs have been a cornerstone of large vocabulary speech recognition, revolutionizing the way extensive vocabularies are processed. This inherent capability has been instrumental in advancing the accuracy and efficiency of large vocabulary speech recognition systems, contributing to their widespread adoption.<ref name=":0" /> * In 1996, Bourlard and Dupont marked a significant milestone by employing HMMs in the domain of automatic subtitle generation. Their pioneering work showcased the versatility of HMMs in transcribing spoken content into textual subtitles. This breakthrough enhanced accessibility and facilitated the dissemination of audiovisual content, making it more inclusive and accessible to diverse audiences.<ref>H. Bourlard and S. Dupont, βA new ASR approach based on independent processing and recombination of partial frequency bands.β Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pages 426--429, Philadephia, Pennsylvania, October 1996.</ref> * In the domain of continuous speech recognition , these models demonstrate notable expertise in the representation of acoustic features inherent to speech and the formulation of language models conducive to the process of decoding spoken utterances. The incorporation of HMMs within continuous speech recognition systems has played a pivotal role in facilitating the attainment of robust and precise transcription capabilities. Consequently, this integration has substantially broadened the scope of applications within the field of voice recognition and transcription.<ref>Young S J, Evermann G, Gales M J F, et al. The HTK book version 3.4 Manual[J]. Cambridge University Engineering Department, Cambridge, UK, 2006.</ref> * While the landscape of speech recognition has evolved with the advent of deep learning and neural networks, HMMs have not faded into obsolescence. In 2021, MIT researchers showcased the enduring importance of HMMs by integrating them into a speaker-adapted HMM-GMM (Hidden Markov Model-Gaussian Mixture Model) Automatic Speech Recognition (ASR) system. This groundbreaking research involved training HMMs using audio data and source language VTT subtitles, leveraging the KALDI framework and Mel-Frequency Cepstral Coefficients (MFCC) features. The outcome was a model that seamlessly combined speech recognition and syntax translation, highlighting the adaptability and effectiveness of HMMs in addressing complex tasks at the intersection of speech processing and natural language understanding.<ref>Salesky E, Wiesner M, Bremerman J, et al. The multilingual tedx corpus for speech recognition and translation[J]. arXiv preprint arXiv:2102.01757, 2021.</ref>
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