Forschungszentrum Telekommunikation Wien [Telecommunications Research Center Vienna] Interfaces between Speech and Non-Speech Audio Technology Michael Pucher (FTW Vienna, ICSI Berkeley)

© ftw Contents  Text-to-Speech Synthesis (TTS)  Automatic Speech Recognition (ASR, STT)  Dialog Systems  Multimodal Mobile Applications  Resources

© ftw Auditory representations Affective states and attitudes Speaker characteristics Structural prosodic elements Pragmatics and discourse Sound signals Music Perspectival, spatial cues Non-linguistic Paralinguistic Linguistic Lexical semantics and syntax TTS ASR Dialog Systems

© ftw TTS Examples  16kHz natural voice  16kHz unit selection synthesis (server-based)  8kHz diphone-based synthesis with lexicon (embedded or distributed)  8kHz diphone-based synthesis without lexicon (embedded)  Application specific lexicon tSE-r6ld a:R fo:rd -Gerald R. Ford  tSE-r6ld a:R fo:rd

© ftw TTS Evaluation

© ftw TTS and Non-Speech Audio TTSFEATURESTATUS COMBINATION WITH NON- SPEECH AUDIO Comprehensible TTS Low word- error-rate Solved Diphone based TTS TTS provides lexical information Add structural prosodic elements Natural TTS Single style prosody Solved Unit selection TTS provides structural prosodic elements Add affective states and attitudes Expressive TTS Various prosodic styles Not solved ? Add pragmatic information, dialog turns

© ftw Limited Expressiveness of Speech 1  Limited expressiveness of Expressive TTS = Limited expressiveness of speech  Limited expressiveness of speech because of unlimited expressiveness 1 of speech -Because everything is expressible in language, the messages are less useful for certain purposes (too complex) -Simpler, less expressive codes (sounds, icons) may be used in context and lead to shorter messages  Disadvantages of speech -Seriality -Non-universality

© ftw Types of ASR and Applications  Isolated word recogniton  Large vocabulary Speech recognition  Conversational Speech recognition  Speech Recognition in noisy environments Car navigation Meeting transcription Command & control Broadcast news transcription Speaker dependent or speaker independent

© ftw Other Related Technologies  Speech -Speaker verification  NLP -Dialog act detection -Topic detection

© ftw Music Information Retrieval (MIR)  Query By Humming (Fraunhofer) -Non-speech sound as an input pattern to search for other non-speech sounds -  Performer Style Identification  Melody and Rhythm Extraction  Music Similarity  Genre Classification

© ftw Dialog Systems - ASR  3 Types of Recognition in state- of-the-art Dialog Systems -Isolated word -Recognition grammar -Statistical Language Model (SLM) + grammar for more robustness move forward backward exit quit „um ah to san francisco from new york“ 1. Apply SLM 2. Apply grammar on results of SLM

© ftw Dialog Systems – TTS and Audio  Loquendo TTS Mixer -Play and mix TTS and audio files -Fadein, fadeout -Pause and resume -Record Paolo Massimino : Loquendo S.p.A. From Marked Text to Mixed Speech and Sound

© ftw Dialog Management 1  Usages of non-speech audio -Replace prompts -Indicate dialog turns and dialog states -Indicate menu structure (3Daudio) -Create listen & feel of the application -System response time  Questions -Bargein, Streaming and Standardization

© ftw Dialog Management 2  A good bad example -Uses only speech -Audio enhancement for transitions -Audio enhancement for states Bob Cooper : Avaya Corporation A Case Study on the planned and actual Use of Auditory Feedback and Audio Cues in the Realization of a Personal Virtual Assistant

© ftw Dialog Managment 3  VoiceXML Version 2.0 -W3C (Word Wide Web Consortium) standard for voice dialog design -Form filling paradigm similar to web forms  Synthesis Markup Language (SSML) Version 1.0 good morning Any female voice here. A female child voice here.

© ftw Limited Expressiveness of Speech 2  Limited expressiveness of human-machine voice dialog compared to a natural dialog -Natural dialog is probable multimodal -Role of non-speech sound in human communication

© ftw The Importance of Multimodality for Mobile Applications  Multimodal communication is perceived as natural  Disadvantages of unimodal interfaces for mobile devices -Small displays -No comfortable alphanumeric keyboards -Visual access to the display is not always possible  Disadvantages cannot be overcome by increasing processor and memory capabilities

© ftw Multimodal Dialog Managment  Speech Application Language Tags (  Possible combination with non-speech audio at all states and transitions  Similar to (unimodal) dialog systems Minhua Ma : University of Ulster Paul Mc Kevitt : University of Ulster Lexical Semantics and Auditory Display in Virtual Storytelling

© ftw Asymmetric Multimodality  For Multiparty applications -Users select preferred modalities (e.g. speech, visual, music?) -System is able to translate content from one modality to another  MONA – Mobile Multimodal Next Generation Applications -Multiuser quiz application InputOutput Preference=SpeechOutput Preference=Visual Speech Speech-To-Text TextText-To-SpeechText

© ftw Resources  TTS -Festival 2.0, to build unit selection voices -Festival Lite, for embedded TTS -FreeTTS, a Java speech synthesizer -The Mbrola project, many synthetic voices available  ASR -Sphinx -Htk  Multimodal Systems -SALT implementations

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