Qian et al., 2017 - Google Patents
Improving native language (l1) identifation with better vad and tdnn trained separately on native and non-native english corporaQian et al., 2017
- Document ID
- 3721836241849806317
- Author
- Qian Y
- Evanini K
- Lange P
- Pugh R
- Ubale R
- Soong F
- Publication year
- Publication venue
- 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
External Links
Snippet
Identifying a speaker's native language (L1), ie, mother tongue, based upon non-native English (L2) speech input, is both challenging and useful for many human-machine voice interface applications, eg, computer assisted language learning (CALL). In this paper, we …
- 230000000875 corresponding 0 abstract description 8
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- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
- G10L15/187—Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
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- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
- G10L15/19—Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
- G10L15/197—Probabilistic grammars, e.g. word n-grams
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