Material for Automatic Phonetic Transcription of Speech Recorded in Various Conditions

Kostek B, Plewa M, Czyzewski A. Material for Automatic Phonetic Transcription of Speech Recorded in Various Conditions. In: AES Convention 141. Audio Engineering Society; 2016. Paper 9648. Available from: https://aes.org/publications/elibrary-page/?id=18452

@inproceedings{Kostek2016_18452,
  author = {Kostek, Bozena and Plewa, Magdalena and Czyzewski, Andrzej},
  title = {{Material for Automatic Phonetic Transcription of Speech Recorded in Various Conditions}},
  booktitle = {AES Convention 141},
  note = {Paper 9648},
  year = {2016},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18452}
}

TY  - CPAPER
TI  - Material for Automatic Phonetic Transcription of Speech Recorded in Various Conditions
AU  - Kostek, Bozena
AU  - Plewa, Magdalena
AU  - Czyzewski, Andrzej
T2  - AES Convention 141
M1  - Paper 9648
PY  - 2016
DA  - 2016/09/06
UR  - https://aes.org/publications/elibrary-page/?id=18452
PB  - Audio Engineering Society
LA  - en
AB  - Automatic speech recognition (ASR) is under constant development, especially in cases when speech is casually produced or it is acquired in various environment conditions, or in the presence of background noise. Phonetic transcription is an important step in the process of full speech recognition and is discussed in the presented work as the main focus in this process. ASR is widely implemented in mobile devices technology, but the need is also encountered in applications such as automatic recognition of speech in movies for non-native speakers, for impaired users, and as a support for multimedia systems. This work contains an attempt to analyze speech recorded in various conditions. First, audio and video recordings of specially constructed list of words in English were prepared in order to perform dedicated audio and video analyses in the future stages of the research aiming at audio-visual speech recognition systems (AVSR) development. A dataset of audio-video recordings was prepared and examples of analyses are described in the paper.
ER  -