Feature Extraction Using Hidden Markov Model for a Phonetic Process

<p>Speech is one of the primary forms of communication among humans. In real life, a dictionary is used to seek the pronunciation of a complex word; but, for computers, this look-up table is called a phonetic dictionary. A speech recognition process tags a word-utterance to its phoneme structure, thereby returning the grapheme representation. However, the speech recognition process is challenging because of the contextual relationship between words and sentences, dependent on speakers&rsquo; intentions. Further, factors influencing time, accents, noisy environment, and data security impose accuracy threats. The present research study proposes a new hybrid speech recognition model by considering three significant aspects: sound generation through phonetic representation, sound acoustics for transmission, and sound reception on how the sound is received. These steps are achieved through a speech-to-text model divided into various stages such as noise removal, speech-pause detection, feature extraction through framing, and windowing by adopting Hidden Markov Model (HMM). The implementation is performed on a phonetic tool, Praat. The robustness of the model is estimated using evaluation metrics such as f-measure and accuracy, resulting in 98% and 99% scores, respectively. Thus, the proposed approach efficiently transforms the spoken words into their corresponding text.</p>