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Customized deep learning based Turkish automatic speech recognition system supported by language model [PDF]

PeerJ Computer Science
Background In today’s world, numerous applications integral to various facets of daily life include automatic speech recognition methods. Thus, the development of a successful automatic speech recognition system can significantly augment the convenience ...
Yasin Görmez
doaj   +3 more sources

KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition

Applied Sciences (Switzerland), 2020
This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed
Seung Yun, Min-Kyu Lee, Sang-Hun Kim
exaly   +3 more sources

Automatic Speech Recognition from Neural Signals: A Focused Review

Frontiers in Neuroscience, 2016
Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might
Christian Herff, Tanja Schultz, Schultz Tanja   +2 more
exaly   +3 more sources

THE ALGORITHM FOR DETERMINING WORD BOUNDARIES IN CONTINUOUS SPEECH IN TERMS OF NOISE POLLUTION BASED ON THE ANALYSIS OF THE ENTROPY OF THE SPEECH SIGNAL

Современная наука и инновации, 2022
Automatic speech recognition, especially in noisy environments, is a complex task. The most important stage of automatic speech recognition is the correct definition of word boundaries in the speech stream.
Andrey Sergeyevich Karpov, Galina Vyacheslavovna Shagrova, Victoria Igorevna Drozdova   +2 more
doaj   +1 more source

Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case

Mathematics, 2023
Recently, there has been a growth in the number of studies addressing the automatic processing of low-resource languages. The lack of speech and text data significantly hinders the development of speech technologies for such languages.
Irina Kipyatkova, Ildar Kagirov
doaj   +1 more source

Feature compensation based on the normalization of vocal tract length for the improvement of emotion-affected speech recognition

EURASIP Journal on Audio, Speech, and Music Processing, 2021
The performance of speech recognition systems trained with neutral utterances degrades significantly when these systems are tested with emotional speech. Since everybody can speak emotionally in the real-world environment, it is necessary to take account
Masoud Geravanchizadeh, Elnaz Forouhandeh, Meysam Bashirpour   +2 more
doaj   +1 more source

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

EURASIP Journal on Advances in Signal Processing, 2007
We present the use of stethoscope and silicon NAM (nonaudible murmur) microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible) speech ...
Hiroshi Saruwatari, Kiyohiro Shikano, Tomomi Kaino, Panikos Heracleous   +3 more
doaj   +2 more sources

Automatic Speech Recognition Using Limited Vocabulary: A Survey

Applied Artificial Intelligence, 2022
Automatic Speech Recognition (ASR) is an active field of research due to its large number of applications and the proliferation of interfaces or computing devices that can support speech processing.
Jean Louis K. E Fendji, Diane C. M. Tala, Blaise O. Yenke, Marcellin Atemkeng   +3 more
doaj   +1 more source

Assessing the accuracy of automatic speech recognition for psychotherapy

npj Digital Medicine, 2020
Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings ...
Adam S. Miner, Albert Haque, Jason A. Fries, Scott L. Fleming, Denise E. Wilfley, G. Terence Wilson, Arnold Milstein, Dan Jurafsky, Bruce A. Arnow, W. Stewart Agras, Li Fei-Fei, Nigam H. Shah   +11 more
doaj   +1 more source

Automatic depression recognition by intelligent speech signal processing: A systematic survey

CAAI Transactions on Intelligence Technology, 2023
Depression has become one of the most common mental illnesses in the world. For better prediction and diagnosis, methods of automatic depression recognition based on speech signal are constantly proposed and updated, with a transition from the early ...
Pingping Wu, Ruihao Wang, Han Lin, Fanlong Zhang, Juan Tu, Miao Sun   +5 more
doaj   +1 more source