PPT-Speech Recognition and HMM Learning

Author : faustina-dinatale | Published Date : 2017-06-06

1 Speech Recognition and HMM Learning Overview of speech recognition approaches Standard Bayesian Model Features Acoustic Model Approaches Language Model Decoder

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Speech Recognition and HMM Learning: Transcript

1 Speech Recognition and HMM Learning Overview of speech recognition approaches Standard Bayesian Model Features Acoustic Model Approaches Language Model Decoder Issues Hidden Markov Models. within . Noisy Environments. .. Florian . Bacher. & Christophe Sourisse. [623.400] Seminar in Interactive Systems. Agenda. Introduction. Methodology. Experiment Description. Implementation. Results. BY:. PRATIBHA CHANNAMSETTY. SHRUTHI SAMBASIVAN. Introduction. What is speech recognition?. Automatic speech recognition(ASR) is the process by which a computer maps an acoustic speech signal to text.. Tomer. . Meshorer. Agenda. This presentation describes the use of speech recognition for:. . HCI . for spastic . dysarthria. patients . [M. Hasegawa-Johnson]. Identify progression of . P. arkinson disease using speech signal[A. Morten Nielsen,. CBS, . Department of Systems Biology, . DTU. Objectives. Introduce Hidden Markov models and understand that they are just weight matrices with gaps. How to construct an HMM. How to . MaxEnt Re-ranked Hidden Markov Model. Brian Highfill. Part of Speech Tagging. Train a model on a set of hand-tagged sentences. Find best sequence of POS tags for new sentence. Generative Models. Hidden Markov Model HMM. “Noisy channel” model of speech. Speech feature extraction. Acoustic wave form. Sampled at 8KHz, quantized to 8-12 bits. Spectrogram. Time. Frequency. Amplitude. Frame. (10 ms or 80 samples). Feature vector. Behrooz Chitsaz. Director, IP Strategy. Microsoft Research. behroozc@microsoft.com. Frank Seide. Lead Researcher. Microsoft Research. fseide@microsoft.com. Kit Thambiratnam. Researcher. Microsoft Research. MUSIC 318 MINI-COURSE ON SPEECH AND SINGING. Science of Sound, Chapter 16. The Speech Chain. , Chapters 7, 8. SPEECH RECOGNITION. OUR ABILITY TO RECOGNIZE THE SOUNDS OF LANGUAGE IS TRULY PHENOMENAL. WE CAN RECOGNIZE MORE THAN 30 PHONEMES PER SECOND. Introduction. History. Modern Applications. Case Study. Ethical Analysis. Overview. Voice recognition . Speech recognition . -. converts . spoken words to text. The term "voice recognition" is sometimes used to refer to recognition systems that must be trained to a particular . Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections June 21 ACL 2011 Slav Petrov Google Research Dipanjan Das Carnegie Mellon University Part-of-Speech Tagging Portland has a thriving music scene . New-Generation Models & Methodology for Advancing . Speech Technology . and Information Processing. Li Deng . Microsoft Research, Redmond, . USA. CCF, . Beijing. , July . 8. , 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . Overview. How . is. . it. . possible. to . recognize. a music clip?. Shazam. Speech vs. music. Speech . recognition. : the . basics. Speech . recognition. : products. Music. A . recognition. . module. Srikar Nadipally. Hareesh . Lingareddy. What is Speech Recognition. A Speech Recognition System converts a speech signal in to textual representation. 3. of 23. Types of speech recognition. Isolated words. a finite state automata with nodes that represent hidden states (that is, things we cannot necessarily observe, but must infer from data) and two sets of links. transition – probability that this state will follow from the previous state.

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