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Wreck a nice beach: adventures in speech recognitionStephen MarquardCentre for Educational Technology, University of Cape Townstephen.marquard@uct.ac.zaDepartment of Computer ScienceSeminar, April 2011,[object Object]
Overview,[object Object],Project goals,[object Object],Speech recognition,[object Object],Acoustic modelling,[object Object],Language modelling,[object Object],Integration into a lecture capture system,[object Object]
Project goals,[object Object],Integrate speech recognition into a lecture capture system:,[object Object],Opencast Matterhorn,[object Object],CMU Sphinx ASR engine,[object Object],Generate automatic transcripts of recorded lectures,[object Object],Allow users to correct and improve the transcripts (crowdsourcing),[object Object],Use feedback to improve recognition accuracy (of the same, similar or subsequent recordings),[object Object],Experiment and implement at UCT,[object Object]
Why is it important?,[object Object],Video and audio is more useful if you can:,[object Object],Navigate it easily,[object Object],Locate relevant recordings from a large set,[object Object],Use by students:,[object Object],Catch up on missed lectures (continuous play or read the transcript),[object Object],Revision: jump to a particular point or find the lectures which cover topic X,[object Object],On the public web:,[object Object],Discoverability (search indexing),[object Object]
Easy or hard?,[object Object],Easiest: small, fixed vocabulary, prescriptive grammar, discrete words, known audio conditions (command-and-control systems),[object Object],Dictation applications in a specific domain, e.g. Dragon Naturally Speaking,[object Object],Hardest: speaker-independent, large vocabulary continuous speech recognition, adverse or unknown audio conditions,[object Object]
Why is it hard?,[object Object],People have huge amounts of prior experience and a rich (complex) understanding of context,[object Object],Modelling of context in ASR engines is currently very limited,[object Object],Even people misrecognize speech (e.g. new / foreign accents, specialized terminology, background noise),[object Object]
Speech recognition,[object Object],Wreck a nice beach 			… you sing calm incense,[object Object],Reckon eyes peach,[object Object],Recognize speech,[object Object],				… using common sense,[object Object]
Early history,[object Object],First known device 1952 (digits),[object Object],Above: IBM Shoebox, 1961,[object Object],http://www-03.ibm.com/ibm/history/exhibits/specialprod1/specialprod1_7.html,[object Object]
Linguistics vs statistics,[object Object],	Early approaches tried to recognize individual phonemes (phonetic units) and hence the words they formed.,[object Object],	But not very successfully.,[object Object]
Airplanes don’t flap their wings,[object Object],	“Every time I fire a linguist, my system improves”,[object Object],	Fred Jelinek,[object Object],	1985/1988,[object Object]
Speech recognition pipeline,[object Object],Audio (signal processing, extract features),[object Object],Acoustic model (features to phonemes),[object Object],Pronunciation dictionary (lexicon),[object Object],Language model (likelihood of words),[object Object],Confusion lattice (possible options),[object Object],Results > confidence score,[object Object]
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-345-automatic-speech-recognition-spring-2003/lecture-notes/lecture1.pdf,[object Object]
Hidden Markov Models,[object Object],HMMs model transition probabilities:,[object Object],Alice talks to Bob three days in a row and discovers that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment.,[object Object],Alice has a question: what is the most likely sequence of rainy/sunny days that would explain these observations?,[object Object],http://en.wikipedia.org/wiki/Viterbi_algorithm,[object Object]
Training in action,[object Object],	“training 3 (decision) trees to depth 20 from 1 million images takes about a day on a 1000 core cluster”http://research.microsoft.com/pubs/145347/BodyPartRecognition.pdf,[object Object]
Characteristics of the field,[object Object],	“the standard approach in our field [is] state-of-the-art system A is gently perturbed to create system B, resulting in a relative decrease in error rate of from 1 to 10%”,[object Object],Borlard, Hermansky and Morgan. Towards increasing speech recognition error rates, 1996.,[object Object],Algorithmic, drawing on many disciplines (especially signal processing, statistics, linguistics, natural language processing),[object Object],Empirical: lots of different algorithms and optimizations,[object Object],Almost no theory to describe why particular approaches work better than others, or how to find optimal solutions,[object Object],Massive infrastructure is a big advantage: large and varied data sets, significant computing resources.,[object Object]
Audio issues,[object Object],Bandwidth,[object Object],Recording noise,[object Object],Ambient noise,[object Object],Reverberation,[object Object],Microphones,[object Object],Microphone arrays,[object Object]
Wreck a nice beach: adventures in speech recognition
Acoustic models,[object Object],Generated from a corpus of recorded, transcribed audio,[object Object],Both artificial and natural corpuses(TIMIT, Broadcast News, Meetings),[object Object],Audio needs to match the application,[object Object],Audio bandwidth = ½ sampling rate,[object Object],Phone speech (sampled 8 KHz, bandwidth 4 KHz),[object Object],Microphone speech (sampled 16 KHz, bandwidth 8 KHz, typical analysis on 130 Hz – 6800 Hz),[object Object],There is a South African corpus of phone speech ,[object Object],But no South African corpus of microphone speech ,[object Object]
The TIMIT audio corpus,[object Object],	0 47719 She had your dark suit in greasy wash water all year,[object Object],2214 4428 she,[object Object],4428 8316 had,[object Object],7308 9691 your,[object Object],9691 15331 dark,[object Object],15331 19634 suit,[object Object],20929 22453 in,[object Object],22453 27697 greasy,[object Object],27697 32326 wash,[object Object],33120 36575 water,[object Object],37597 39644 all,[object Object],39644 43982 year,[object Object],0 2214 h#,[object Object],2214 3744 sh,[object Object],3744 4428 ax-h,[object Object],4428 5229 hv,[object Object],5229 6927 ae,[object Object],6927 7308 dcl,[object Object],7308 8316 jh,[object Object],8316 9691 axr,[object Object],9691 11697 dcl,[object Object],11697 12114 d,[object Object],12114 13075 aa …,[object Object],Word and phoneme alignment by timecode.,[object Object],630 speakers from 8 US dialect regions, speaking 10 sentences each.,[object Object]
Dialect regions,[object Object],The Nationwide Speech Project: A new corpus of American English dialects,[object Object],http://web.mit.edu/~nancyc/Public/Papers/Clopper_Pisoni_06_SC.pdf,[object Object]
Crowdsourcing the creation of a GPL speech corpus and open source acoustic models (Sphinx, ISIP, Julius, HTK).,[object Object],	An important effort, but still small (84 hours at Dec 2010)www.voxforge.org,[object Object]
Language modelling,[object Object],Pronunciation dictionary (lexicon),[object Object],	TOMATO  T AH0 M EY1 T OW2,[object Object],		TOMATO(1)  T AH0 M AA1 T OW2,[object Object],Language model: a statistical sequence model of words. Trigram models (3 words) are common:,[object Object],	-2.0998 YORK MONEY FUND ,[object Object],	-0.0798 YORK HEDGE FUND ,[object Object],	-0.1392 YORK MUTUAL FUND ,[object Object]
Statistical sequence models,[object Object],Truly Madly _____,[object Object],Widely used,[object Object],Applications,[object Object],Auto-suggest,[object Object],Spell-checkers,[object Object],Lossless compression,[object Object],Machine translation,[object Object],Language models for speech recognition,[object Object],Probability of token w in context of preceding tokens c, e.g. P(deeply), given “truly madly”,[object Object]
Context is king,[object Object],Micro-context (e.g. bi- and trigrams),[object Object],	United Kingdom,[object Object],	United Airlines,[object Object],	United Arab Emirates,[object Object],Long-range context,[object Object],	“Cricket and rugby are amongst the most popular sports in the United _________”,[object Object],(example from The Sequence Memoizer, Wood et al, 2011).,[object Object]
Wreck a nice beach: adventures in speech recognition
Characteristics of language,[object Object],Power law frequency / rank distribution. Zipf’s law:,[object Object],	“given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table”,[object Object],http://en.wikipedia.org/wiki/Zipf’s_law,[object Object],Also more frequent words are shorter.,[object Object]
How to get large language data sets,[object Object],Linguistic Data Consortium(by subscription, restricted),[object Object],Some other more specialized corpora,[object Object],Microsoft (free, restricted),[object Object],Google (Creative Commons license),[object Object],Wikipedia (CC / GFDL license),[object Object]
Using Wikipedia as a language resource,[object Object],Download a snapshot (6G compressed),[object Object],Convert from XML and markup to plain text,[object Object],Create dictionaries of target size (by word frequency),[object Object],Create language models of target size,[object Object],Approximately equal in size to English Gigaword Corpus,[object Object]
Grid computing for language modelling,[object Object],For when you need lots of RAM and/or lots of CPU,[object Object],www.sagrid.ac.za,[object Object],ICTS at UCT: Tim Carr, Andrew Lewis,[object Object]
Accounting for context: LM adaptation,[object Object],Adapt a language model to more closely resemble the target speech,[object Object],Using related text for,[object Object],Topic modelling (vocabulary, concepts),[object Object],Style-of-speech modelling,[object Object],	“ok and um it's quite useful to have a very good diagnostic test of of acute hepatitis um you know to prevent kind of unnecessary um surgery um so hepatitis is really one um example of a cause of acute abdominal pain that doesn't need surgery”,[object Object]
What’s special about lectures?,[object Object],Possibly helpful assumptions:,[object Object],Coherent topic(s) within a course,[object Object],One lecturer presents many lectures,[object Object],Specialized vocabulary,[object Object],Spoken speech different to written speech,[object Object]
Wreck a nice beach: adventures in speech recognition
Using Wikipedia for LM adaptation,[object Object],Goal is to adapt a “standard” LM to be specific to the topic of the audio,[object Object],Start somewhere: title, keywords, text from slides,[object Object],Select a set of documents, adapt the LM,[object Object],Using wikipedia, select by similarity: identify the set of documents most closely related to the starting point or keywords,[object Object]
Vector space modelling,[object Object],Represents documents as n-dimensional vectors (n terms),[object Object],Document similarity established by comparing vectors, producing a similarity score.,[object Object],Gensim VSM toolkit: independent of corpus size (so good for wikipedia),[object Object],LSI, LDA, TF-IDF measures. ,[object Object],Create a “similarity crawler” to build a corpus of documents related to the topic,[object Object]
Metrics,[object Object],Perplexity (average number of guesses required),[object Object],Word Error Rate (edit distance: insertions, deletions, substitutions),[object Object],Information Retrieval: precision and recall,[object Object],What’s sufficient? Need to close an accuracy gap of ,[object Object],Munteanu research: %WER for a transcript,[object Object]
What is lecture capture?,[object Object],Largely automated:,[object Object],[object Object]
 Processing
 OutputRecreates the lecture experience by recording:,[object Object],[object Object]
 video
 screen output (VGA)www.opencastproject.org,[object Object]
Licensing constraints,[object Object],Opencast Matterhorn is licensed under the ECL open source license (similar to Apache 2.0 license),[object Object],Allows closed commercial derivatives,[object Object],Therefore cannot use software or datasets which are non-commercial or research-only.,[object Object],Can use Apache, BSD, LGPL, maybe GPL code and data.,[object Object]
Speech recognition software ecosystem,[object Object],Licensing and patents,[object Object],Closed,[object Object],Proprietary,[object Object],FOSS,[object Object],Open,[object Object]
Opencast in action,[object Object]

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Wreck a nice beach: adventures in speech recognition

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