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- | [[Progress Report]] | + | The ASR module based on Hidden-Markov models transforms input speech into text, providing a front-end between the user and the Czech demonstrator. The ASR output is smoothed into a form close to standard written text using statistical machine translation in order to bridge the gap between dis-fluent spontaneous speech and a standard grammatical sentence. |
+ | Results of the part-of-speech tagging are passed on to the Maximum Spanning Tree Syntactic Parsing module. A tectogrammatical representation of the utterance is constructed once the syntactic parse is available. Annotation of the meaning at tectogrammatical layer is more explicit than its syntactic parse and lends itself for information extraction. The Named Entity Recognition module then marks personal names and geographical locations. A Dialog Act classifier combines lexical and morphological features to assess the type of user utterance (such as question, acknowledgement, | ||
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- | ====== Description | + | In addition, when generating the system response, the dialogue manager will pass through the NLG module the information about the appropriate communicative function along with the sentence that is to be generated. NLG is also used to generate paraphrases |
- | The Czech version of the Companion deals with the Reminiscing about User's Photos scenario, taking advantage of data recorded in first phase of the project. The basic architecture is same as of the English version, i.e. set of modules communicating through the Inamode Relayer (TID) backbone; however the set of modules is different (see Figure 1). Regarding the physical settings, the Czech version runs on two notebook computers connected by a local network; one can be seen as a Speech Client, running modules dealing with ASR, TTS and ECA, second as an NLP Server. | ||
- | The dialog is driven by a dialog manager component by USFD (originally developed for the English Senior Companion prototype), we supply the transition network (DAFs). The selection is backed by (a) appropriateness for the type of dialog we aim for (the corpus reveals frequent reoccurring topics to be handled by DAFs) , (b) availability of mature package within time frame that allows for integration, | + | < |
- | Our DAFs covering selected topics contain not only Companion | + | The Czech Companion |
+ | taking advantage of the data collected in the first phase of the project (using a Wizard-of-Oz setting). The full recorded corpora was transcribed, a manual speech reconstruction was done on 92.6% of utterances((Manual speech reconstruction is still in progress.)) and a pilot dialog acts annotation was performed on a sample of 1000 sentences. | ||
- | For a sample dialogue, see the Scenario Brief below. | + | The architecture is the same as in the English version, i.e. a set of modules communicating through the Inamode (TID) backbone. However, the set of modules is different, see Figure 1. Regarding the physical settings, the Czech version runs on two notebook computers connected by a local network. One serves as a Speech Client, running modules dealing with ASR, TTS and ECA; the other one as an NLP Server. |
- | {{user: | + | The NLU pipeline, DM, and NLG modules at the NLP Server are implemented using a CU's own TectoMT platform that provides access to a single |
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- | ===== Automatic Speech Recognition (WP 5.1)===== | + | |
- | features: improved language models, real-time speaker adaptation | + | |
- | performance indicator: WER | + | |
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- | ===== Speech Reconstruction (WP 5.2) ===== | + | |
- | features: omit filler phrases, remove irrelevant speech events, handle false starts, repetitions, and corrections, | + | |
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- | ===== Morphology Analyzer and POS tagging (WP 5.2) ===== | + | |
- | features: coverage of photo-pal domain, domain adapted tagger | + | |
- | performance indicator: OOV rate, accuracy (Morce podle webu 95.1%) | + | |
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- | ===== Syntactic Parsing (WP 5.2) ===== | + | |
- | features: induce dependencies and labels | + | |
- | performance indicator: accuracy (correctly induced edges, labels) | + | |
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- | ===== Semantic Parsing (WP 5.2) ===== | + | |
- | features: assignment of semantic roles (69 roles), coordinations, | + | |
- | performance indicator: accuracy (correctly induced edges, labels) | + | |
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- | ===== Information Extraction (WP 5.2) ===== | + | |
- | features: template based identification of predicates | + | |
- | covering predicates from before-mentioned set of DAFs. | + | |
- | performance indicator: accuracy | + | |
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- | ===== Named Entities Recognition (WP 5.2) ===== | + | |
- | features: detect person names, geographical locations, organization names | + | |
- | performance indicator: f-measure | + | |
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- | ===== Dialog Act Tagging (WP 5.2) ===== | + | |
- | features: domain tailored tag-set (variation of DAMSL-SWBD) | + | |
- | performance indicator: accuracy | + | |
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- | ===== Dialog Manager (WP 5.3) ===== | + | |
- | features: integrated DAF-based dialog manager from previous English prototype, | + | |
- | manual creation of DAFs covering following topics: Person_retired, | + | |
- | performance indicator: acceptability | + | |
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- | ===== Natural Language Generation (WP 5.4) ===== | + | |
- | features: adding of functional words, morphological adjustments, | + | |
- | performance indicator: BLEU score | + | |
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- | ===== Emotional TTS (WP 5.5) ===== | + | |
- | features: emotions will be expressed implicitly, | + | |
- | performance indicator: listening tests | + | |
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- | ===== Emotional Avatar Integration (WP 5.5) ===== | + | |
- | features: new Czech female voice with affective features will be integrated with the TID avatar | + | |
- | performance indicator: subjective evaluation | + | |
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- | ====== Scenario Brief ====== | + | |
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- | C1 Dobrý den, jak se jmenujete? (intro-daf-based) | + | |
- | H1 Marie. | + | |
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- | C2 Těší mě. Já jsem Pavla a chtěla bych Vás blíž poznat. Jsem ráda, že mi ukážete Vaše fotografie. | + | |
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- | C2 Co je tohle? (photo_maintenance) | + | |
- | H2 To je moje rodina, rodiče, sestra, její manžel, moje dvě děti a její dvě děti. | + | |
- | H2 Byli u mě na Vánoce na návštěvě. | + | |
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- | C3 Trávíte Vánoce takhle pohromadě? | + | |
- | H3 Teď už ne. | + | |
- | H3 Teď už všechny moje dětičky vylétaly z hnízda. | + | |
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- | C4 Jak se jmenuje váš manžel? | + | |
- | H4 Manžel už nežije, jmenoval se Karel Šístek. | + | |
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- | C5 Máte ráda Vánoce? | + | |
- | H5 Teď už ne. | + | |
- | H5 Měla jsem je ráda, když byly děti malé. | + | |
- | H5 Teď už to ale není ono. | + | |
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- | C6 Takže teď trávíte Vánoce sama? (ask_on_detail-daf-based) | + | |
- | H6 Nejsem úplně sama, chodím každé Vánoce k dceři, ale už to není, jak to bývalo, když byly doma malé děti. | + | |
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- | C7 Jak děti vyrostou, tak má člověk najednou příliš mnoho času. | + | |
- | C7 Kdo je kdo na té fotce? | + | |
- | H7 U stromečku je můj synovec, ta holčička vedle je moje dcera, chlapeček s hračkou je můj syn, vedle je sestra, její manžel a nad ním je můj otec. | + | |
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- | C8 To jste ale velká rodina. | + | |
- | C8 Jak se jmenuje vaše sestra? (companion commenting last utterance | + | |
- | H8 Sestra se jmenuje Marie Havlová, její manžel je Vladimír, ten bohužel nedávno zemřel. | + | |
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- | C9 To je smutné. | + | |
- | C9 Znala jste ho dobře? | + | |
- | H9 Velmi dobře, trávili jsme obě rodiny společně hodně času. | + | |
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- | C10 Máte už nějaká vnoučata? | + | |
- | H10 Mám vnučku Natálku. | + | |
- | H10 Ona je takové živé děťátko, taková neposeda. | + | |
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- | C11 Takže Natálka je neposeda. repeat-rephrase | + | |
- | C11 Máte ještě nějaké další vnučky nebo vnuky? | + | |
- | H11 Čekáme ještě další přírustek. | + | |
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- | C12 Hlídáte je někdy? | + | |
- | H12 Hlídám dost často. | + | |
- | H12 Já mám děti ráda a jsem s nimi moc ráda. | + | |
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- | C13 To je fajn. | + | |
- | C13 S dětmi je legrace. | + | |
- | H13 Ano. | + | |
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- | C Podíváme se na další fotku? | + | |
+ | The Knowledge Base consists of objects (persons, events, photos) that model the information acquainted in the course of dialog. Those objects also provide a very basic reasoning (e.g. accounting for the link between date of birth and age properties). Each object' | ||