Page 1. Prosodie und emotionale Sprachverarbeitung. Elmar Nöth FAU Erlangen-Nürnberg

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1 Page 1 Prosodie und emotionale Sprachverarbeitung Elmar Nöth FAU Erlangen-Nürnberg

2 Page 2 Overview Prosody Automatic Analysis How it started with EVAR Verbmobil: The use of prosody at all levels What are we doing today: Emotion On-/offtalk Language learning Assessment of pathologic speech

3 Prosody in Human Speech Communication Prosody can help to disambiguate Lexical and phrasal accent Phrasing (chunks of speech) Sentence mood Emotion, attitude, foreign accent Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

4 Prosody in Human Speech Communication Prosody can help to disambiguate/assess Lexical and phrasal accent Phrasing (chunks of speech) Sentence mood Emotion, attitude, foreign accent Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

5 Parameters Represented by our Features F0 (fundamental frequency) Energy Duration Speech tempo Pause Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

6 Page 6 Automatic Analysis How it started In the beginning, there was

7 Page 7 Automatic Analysis How it started In the beginning, there was Adam &

8 Page 8 Automatic Analysis How it started In the beginning, there was

9 Page 9 Automatic Analysis How it started In the beginning, there was Erkennen Verstehen Antworten - Rückfragen

10 Page 10 Overview Prosody Automatic Analysis How it started with EVAR Verbmobil: The use of prosody at all levels What are we doing today: Emotion On-/offtalk Language learning Assessment of pathologic speech

11 Prosodic Processing Task: recognizing prosodically marked functions (accents, sentence mood and boundaries) Input: WHG and speech signal Method: neural networks and statistical classifiers Elmar Nöth, Universität Erlangen-Nürnberg" Result: WHG annotated with accent, boundary, information Benefit: Provides prosodic information needed for correct translation of spontaneous speech Verbmobil Final Symposium, 30 July 2000

12 What Linguistic Analysis Really Needs Syntactic Boundaries He saw? the man? with the telescope Prosody cannot help Dialogue Act Boundaries No, I have no time at all on Thursday. D3 But how about on Friday? Dialogue acts are pragmatic units that chunk the input into units which can be processed alone. Prosodic Syntactic Boundaries Of course? not? on Saturday Syntactic boundaries that correlate to the acoustic-phonetic reality; help during analysis within one chunk/dialogue act. Important in spontaneous speech with elliptical utterances. Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

13 Input to Prosody logical lexical acoustic time frame begin end entry score begin end 1 2 the that runs rooms where were when Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

14 Output from Prosody logical lexical... begin end entry... boundary accent question the (G 0.00) (A 0.00) (F 0.01) that (G 0.00) (A 0.00) (F 0.04) runs (G 0.64) (A 1.00) (F 0.89) rooms (G 0.66) (A 1.00) (F 0.91) where (G 0.05) (A 0.20) (F 0.01) were (G 0.41) (A 0.00) (F 0.01) when (G 0.04) (A 0.00) (F 0.01)... Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

15 Speed-up during Parsing BK/ ge: ja S1 kein Problem S4 Herr Müller S4 wann möchten Sie gerne nach Hannover fahren S4 yes S1 no problem S4 mister Mueller S4 when would you like to go to Hannover S4 without boundaries: runtime: 1.31s #edges: 1256 #VITs: 5 with boundaries: runtime: 0.62s #edges: 632 #VITs: 4 speed-up: 53% Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

16 VM Modules which use Prosody Repair Statistical translation Deep analysis Dialogue act based translation Dialogue semantics Generation Elmar Nöth, Universität Erlangen-Nürnberg" Verbmobil Final Symposium, 30 July 2000

17 Page 17 Overview Prosody Automatic Analysis How it started with EVAR Verbmobil: The use of prosody at all levels What are we doing today: Emotion On-/offtalk Language learning Assessment of pathologic speech

18 Page 18 Classification of Spontaneous Emotions Scenario: Children interacting with Sony s Aibo

19 The INTERSPEECH 2009 Emotion Challenge Page 19

20 The INTERSPEECH 2009 Emotion Challenge Page 20

21 ISA-House The intelligent house adapted to seniors Living room with integrated speech controlled assistence system Goal Support of the senior in activities of daily living Challenges Identification of relevant functions Speech recognition of elderly Dialogue modelling Recognition of On-/Off-Talk On-/Offtalk Recording of Data User studies

22 Test persons User studies in the ISA-House Senior advisory board SEN-PRO Focus groups Discussion of properties and functions Preparation of Wizard-of-Oz study Wizard-of-Oz study in the ISA-House Goal: Realistic speech data Simulation of the home assistence system by a human 2 Persons per session to study On-/Off-Talk On-talk: Interaction with the system On-/Offtalk Off-talk: Interaction with someone else

23 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht.

24 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht.

25 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht.

26 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht.

27 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht.

28 Recordings Isa-House Data 5 microphones: 2 headsets und 3 far field microphones 3.5h spontaneous speech Off-Talk: 2891 utterances On-Talk: 2752 utterances Examples for On-Talk Dann schalten wir *des Licht ein, oder? Welchen Sender *ham wir eingestellt? Examples for Off-Talk So, der erinnert mich an einen Termin. On-/Offtalk *Bin g'spannt, welcher Termin er *etsa löscht. Recognition rates of up to 85% in SmartWeb for On-/Offtalk (CL, fusion of prosody, POS, and head orientation)

29 Page 29 Automatic Pronunciation Assessment Vital part of computer-assisted language learning Not only mispronounced phonemes, but also suprasegmental peculiarities Assessment of prosody Comprehensive feature set describing duration, pitch, energy Detection of wrong word accent position For example: Engl. analysis versus German Analyse LDA classifier using 104 features per syllable + a priori prob.: at 2.7% false alarm rate: 34.1% hit rate (1 labeller avg.: 34.9%) Assessment of overall prosody Rhythm of L1 transferred onto L2 serious impact on intelligibility Training data rated by 60 native listeners on likert scale (1-5) Multiple linear regression on > 500 prosodic features: correlation r=0.89 with reference (1 labeller avg.: 0.71)

30 Page 30 Automatic Pronunciation Assessment Vital part of computer-assisted language learning Not only mispronounced phonemes, but also suprasegmental peculiarities Assessment of prosody Comprehensive feature set describing duration, pitch, energy Detection of wrong word accent position For example: Engl. analysis versus German Analyse LDA classifier using 104 features per syllable + a priori prob.: at 2.7% false alarm rate: 34.1% hit rate (1 labeller avg.: 34.9%) Assessment of overall prosody Rhythm of L1 transferred onto L2 serious impact on intelligibility Training data rated by 60 native listeners on likert scale (1-5) Multiple linear regression on > 500 prosodic features: correlation r=0.89 with reference (1 labeller avg.: 0.71)

31 Page 31 Automatic Pronunciation Assessment Vital part of computer-assisted language learning Not only mispronounced phonemes, but also suprasegmental peculiarities Assessment of prosody Comprehensive feature set describing duration, pitch, energy Detection of wrong word accent position For example: Engl. analysis versus German Analyse LDA classifier using 104 features per syllable + a priori prob.: at 2.7% false alarm rate: 34.1% hit rate (1 labeller avg.: 34.9%) Assessment of overall prosody Rhythm of L1 transferred onto L2 serious impact on intelligibility Training data rated by 60 native listeners on likert scale (1-5) Multiple linear regression on > 500 prosodic features: correlation r=0.89 with reference (1 labeller avg.: 0.71)

32 Assessment of pathologic speech Alaryngeal (Substitute) Voice Removal of the larynx due to cancer Breathing is detoured through a tracheostoma

33 Assessment of pathologic speech Alaryngeal (Substitute) Voice Removal of the larynx due to cancer Breathing is detoured through the tracheostoma Speaking is enabled by a substitute voice

34 Assessment of pathologic speech Intelligibility (Laryngectomees) The North Wind and the Sun : 107 words (71 disjoint) Contains all German phonemes Commonly used by speech therapists and phoneticians 41 laryngectomees: 62.0 ± 7.7 years old Word recognition with a unigram language model Speech Technology: phoneme & word recognition, prosody

35 Assessment of pathologic speech Intelligibility (Laryngectomees) speaker regression line word Word accuracy Correlation to human experts:.88 Experts scores

36 Assessment of pathologic speech Intelligibility (Laryngectomees) speaker regression line Word accuracy Correlation to human experts:.88 Experts scores

37 Assessment of pathologic speech Intelligibility (Laryngectomees) speaker regression line Word accuracy Correlation to human experts:.88 Experts scores

38 Assessment of pathologic speech Evaluation of Distinct Aspects The North Wind and the Sun Word accuracy corresponds to intelligibility Intelligibility is influenced by different factors Laryngectomized: disturbing noises, hoarseness, match of breath and sense units Evaluate distinct aspects with acoustic and prosodic parameters

39 Assessment of pathologic speech Evaluation of Distinct Aspects criterion prosodic feature type correlation hoarseness energy -.74 disturbing noises (stoma) energy -.76 agreement of breath and sense units effort pause word duration pause word duration

40 Page 40 Summary Prosody HOW something is said Automatic Analysis in EVAR: Sentence mood for dialogue control Verbmobil: The use of prosody at all levels 10 years later: Classification & Assessment More natural HCI Emotion On-/offtalk Assessment of progress Language learning Pathologic speech

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