Interacting with computers by voice: automatic speech recognition and synthesis
Abstract
This paper examines how people communicate with computers using speech. Automatic speech recognition (ASR) transforms speech into text, while automatic speech synthesis [or text-to-speech (TTS)] performs the reverse task. ASR has been largely developed based on speech coding theory, while simulating certain spectral analyses performed by the ear. Typically, a Fourier transform is employed, but following the auditory Bark scale and simplifying the spectral representation with a decorrelation into cepstral coefficients. Current ASR provides good accuracy and performance on limited practical tasks, but exploits only the most rudimentary knowledge about human production and perception phenomena. The popular mathematical model called the hidden Markov model (HMM) is examined; first-order HMMs are efficient but ignore long-range correlations in actual speech. Common language models use a time window of three successive words in their syntactic-semantic analysis. Speech synthesis is the automatic generation of a speech waveform, typically from an input text. As with ASR, TTS starts from a database of information previously established by analysis of much training data, both speech and text. Previously analyzed speech is stored in small units in the database, for concatenation in the proper sequence at runtime. TTS systems first perform text processing, including "letter-to-sound" conversion, to generate the phonetic transcription. Intonation must be properly specified to approximate…
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Speech input is a well established interaction modality.
“On a different level, speech input is a well established interaction modality [57].”
From Hands-Free Accessible Digital Musical Instruments: Conceptual Framework, Challenges, and Perspectives (2020) · cites this paper as [57]
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- Authors
- O'Shaughnessy, D.
- Venue
- Proceedings of the IEEE
- Published
- 2003-09
- Volume
- 91
- Issue
- 9
- Pages
- 1272-1305
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2001537489
- Cited by
- 132
- References
- 295
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 9 · 2021: 3 · 2020: 6 · 2019: 4 · 2018: 5 · 2017: 2 · 2016: 7 · 2015: 7 · 2014: 8 · 2013: 9 · 2012: 4
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the IEEE (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- speech recognition, text-to-speech, speech coding, cepstral coefficients, hidden Markov models, language models, phonetic transcription, intonation, speech perception
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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