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MAI Internship April-May 2002

MAI Internship April-May 2002. What?. The AST Project promotes development of speech technology for official languages of South Africa SAEnglish, Afrikaans, Zulu, Xhosa, Sesotho Create reusable databases & software Prototype hotel booking dialogue system 2000-2003.

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MAI Internship April-May 2002

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  1. MAI Internship April-May 2002

  2. What? • The AST Project promotes development of speech technology for official languages of South Africa • SAEnglish, Afrikaans, Zulu, Xhosa, Sesotho • Create reusable databases & software • Prototype hotel booking dialogue system • 2000-2003

  3. AST dialogue system: basics Telephone Network DATABASE Speech Synthesis Speech Recognition Dialogue Manager Natural LanguageUnderstanding

  4. AST Speech Database • Use?  input ASR: acoustic training •  output ASR: dictionary • Start from scratch, even for SAE • Telephone data based on SpeechDat • Datasheet utterances • Hierarchical recruiting method • Labeling Tool: PRAAT

  5. Language Spoken Code No. of Speakers 1 English (E) Speech varieties: Mother-tongue English Black English Coloured English Asian English Afrikaans English EE BE CE ASE AE 1500-2000 300-400 300-400 300-400 300-400 300-400 2 isiXhosa (X) XX 300-400 3 Sesotho (S) SS 300-400 4 isiZulu (Z) ZZ 300-400 5 Afrikaans (A) Speech varieties: Mother-tongue Afrikaans Black Afrikaans Coloured Afrikaans AA BA CA 900-1200 300-400 300-400 300-400

  6. AST Speech Database Acoustic signal Manual labour Orthographic annotation Rules & dictionary: Patana Phonemic transcription Forced alignment: HTK Phonetic alignment

  7. AST Speech Recognition • Difficult: • Speaker independent, noisy conditions • Medium-size vocabulary (10.000 words) • Training data sparse • Not so difficult: • Dialogue Manager helps • Phoneme-based HMMs  future diphones • Finite-state language model • Pitch & clicks African languages ignored

  8. AST Natural Language Understanding • Same finite-state network as language model recogniser •  +: all utterances ‘understood’ • -: FSG are limited • Makes no sense to recognise more than we can understand • Semantic labels are activated • Alternative: robust parsing (Phoenix, ATIS)

  9. Meaning Recognised utterance Grammar ID Grammar ID AST Natural Language Understanding Speech Recognition Dialogue Manager NLU FSG

  10. AST Natural Language Understanding • Embedded semantic tags: • ‘drie honderd duisend agt en neëntig’  3 0 0 0 9 8 t1=3 t2=0 t3=0 V6=3 V5=0 V4=0 V3=0 V2=9 V1=8

  11. AST Dialogue Manager • Trade-off: naturalness  response restriction • System-directed: predictability user utterances, simple dialogues • Mixed-initiative: shorter dialogues, more recognition errors • User-initiative: unpopular

  12. AST Dialogue Manager • Design: • Early focus on users and task • Wizard-of-Oz: pay no attention to the man behind the curtain • System-in-the-loop • Finite-state structure because of simplicity and functionality • Possible frame-based approach in future

  13. AST Speech Synthesis • Fixed machine utterances: pre-recorded speech • Database queries: limited-domain synthesis (Festival platform)

  14. Conclusion • Finite-state approach in • Recogniser • NLU component • Dialogue manager • Workable prototype • New fundings 2003

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