Читать книгу The Concise Encyclopedia of Applied Linguistics - Carol A. Chapelle - Страница 217

Developing an effective ASR system poses a number of challenges. They include speech variability (e.g., intra and interspeaker variability such as different voices, accents, styles, contexts, and speech rates), recognition units (e.g., words and phrases, syllables, phonemes, diphones, and triphones), language complexity (e.g., vocabulary size and difficulty), ambiguity (e.g., homophones, word boundaries, syntactic and semantic ambiguity), and environmental conditions (e.g., background noise or several people speaking simultaneously).

Despite these challenges, in recent years numerous companies such as Nuance Communications, Google, Microsoft, Apple, and Amazon have developed and released ASR systems and software packages that have applications in computer system interfaces (e.g., voice control of computers, data entry, dictation), education (e.g., toys, games, language translators, language‐learning software), healthcare (e.g., systems for creating various medical reports, aids for blind and visually impaired patients), telecommunications (e.g., phone‐based interactive voice response systems for banking services, information services), military (e.g., voice control of fighter aircraft), and—more increasingly—consumer products and services (e.g., car navigation systems, household appliances, and mobile devices). Some of the most well‐known ASR software packages include Dragon NaturallySpeaking, Braina, and LumenVox Speech Recognizer, as well as interactive ASR‐supported systems such as Siri, Cortana, and Alexa.