Essays about: "Hidden Markov Models in speech"
Showing result 1 - 5 of 12 essays containing the words Hidden Markov Models in speech.
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1. Automatic Annotation of Speech: Exploring Boundaries within Forced Alignment for Swedish and Norwegian
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : In Automatic Speech Recognition, there is an extensive need for time-aligned data. Manual speech segmentation has been shown to be more laborious than manual transcription, especially when dealing with tens of hours of speech. READ MORE
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2. Normalizing Flow based Hidden Markov Models for Phone Recognition
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The task of Phone recognition is a fundamental task in Speech recognition and often serves a critical role in bench-marking purposes. Researchers have used a variety of models used in the past to address this task, using both generative and discriminative learning approaches. READ MORE
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3. Automatic Speech Recognition Model for Swedish using Kaldi
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the development of intelligent era, speech recognition has been a hottopic. Although many automatic speech recognition(ASR) tools have beenput into the market, a considerable number of them do not support Swedishbecause of its small number. READ MORE
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4. Automatic Speech Recognition in Somali
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : The field of speech recognition during the last decade has left the research stage and found its way into the public market, and today, speech recognition software is ubiquitous around us. An automatic speech recognizer understands human speech and represents it as text. READ MORE
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5. A First Study on Hidden Markov Models and one Application in Speech Recognition
University essay from Linköpings universitet/Matematisk statistik; Linköpings universitet/Tekniska fakultetenAbstract : Speech is intuitive, fast and easy to generate, but it is hard to index and easy to forget. What is more, listening to speech is slow. Text is easier to store, process and consume, both for computers and for humans, but writing text is slow and requires some intention. READ MORE