Presentation on theme: "Information Extraction from Spoken Language Dr Pierre Dumouchel Scientific Vice-President, CRIM Full Professor, ÉTS."— Presentation transcript:
Information Extraction from Spoken Language Dr Pierre Dumouchel Scientific Vice-President, CRIM Full Professor, ÉTS
PUT RAW DATA NOW and then LINK DATA
PUT RAW DATA NOW Text Data (numbers, statistics) Data (audio, video)
LINKED DATA Information is in the relationship between data Find relationship between them
IBMs Watson and Jeopardy
Proposal Information Extraction in radio and television documents – Industrial Partners: CEDROM Sni Irosoft – Universities and Research Center CRIM ÉTS INRS-EMT McGill NSERC Strategic Project Proposal
Process Raw Audio Data Automatic Speech Recognition (ASR) Parsing Indexation ASR Parsing Indexation
Closed-captioning / Subtitling VOICEWRITER
Closed- captioning / Subtitling Done with the help of a VoiceWriter that: – Respeaks – Adds punctuation – Selects proper dictionary – Does not speak during advertising – Wraps up information when more than one speakers speak in the same time or when the speech rate is too fast. – Translates
How to process raw audio data? ASR Parsing Indexation Audio Diarization Audio Diarization Speaker Diarization Speaker Recognition Speaker Role Punctuation Structural Segmentation Structural Segmentation Topic Recognition Topic Recognition
Audio Diarization Aims to segment an audio recording into acoustically homogeneous parts – Distinguish between speech and music – Distinguish between advertising and news
Speaker diarization Aims to segment a speech signal into its speech turns
Speaker Role In broadcast news speech, most speech is from anchors and reporters. The remaining is from excerpts from quotations or interviews and are referred as sound bites. Detecting speaker role is important to improve: – acoustice speech recognizer – information extraction
Punctuation Some language analysis tasks such as parsing and entity extraction needs punctuations (dots and commas) in order to work properly.
Structural Segmentation Sentence segmentation, paragraph segmentation, story segmentation are important features for speech understanding applications from parsing and information extraction at the basic level. This problem is absent in text processing but has to be solved in speech processing.
Topic Spotting Aims to identify the topic of a speech signal. It is useful to adapt the different components of the system as well as to add metatag on a speech signal. Example: La belle ferme le voile – La: the, her – Belle: beautiful, beauty – Ferme: farm, closes – Le: the, his – Voile: veil, blocks the view – Two hypothetic translations: The veil is closed by the beauty The beautiful farm blocks his view
How to improve Information Extraction from speech? By improving ASR Components
How to improve Information Extraction from speech? More data are better data. More similar data are better data. Similar in terms of – Topic – Coming from the same time period. Specifically, more recent. Example: Japan – Prediction of what will happen and who will speaks.
More data are better data Use of the huge amount of web information Use super computer infrastructure in order to model it in a reasonable time: – Compute Canada infrastructure: CLUMEQ – Cluster of university computers
More similar data are better data Exploiting redundancies in different media information: – Anchor speech is predominant. – Reporters often appear at specific times, day after day – Advertisings appear (and repeat) near specific time slot, day after day. – The same news is often reused from one media to another.
Exploiting redundancies in different media information
And then …. ASR Parsing Indexation Audio Diarization Audio Diarization Speaker Diarization Speaker Recognition Speaker Role Punctuation Structural Segmentation Structural Segmentation Topic Recognition Topic Recognition