Sslis
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The document describes the design and development of a Speech to Sign Language Interpreter System (SSLIS). The system uses Sphinx 3.5 for automatic speech recognition (ASR) to convert speech to text. It then maps the recognized text to American Sign Language (ASL) videos stored in a database for translation and live signing. The system aims to improve accessibility for the deaf by providing free, open-source sign language interpretation software.
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The document provides an overview of automatic speech recognition, including: describing the process of speech recognition which involves feature extraction from voice and using acoustic and language models; listing common types like speaker-dependent and independent; discussing applications in areas like dictation, in-car systems, and voice security; and noting both advantages like reducing errors but also challenges involving filtering noise and accommodating various speaking styles.
PERFORMANCE ANALYSIS OF DIFFERENT ACOUSTIC FEATURES BASED ON LSTM FOR BANGLA ...
In this work a new Bangla speech corpus along with proper transcriptions has been developed; also
various acoustic feature extraction methods have been investigated using Long Short-Term Memory
(LSTM) neural network to find their effective integration into a state-of-the-art Bangla speech recognition
system. The acoustic features are usually a sequence of representative vectors that are extracted from
speech signals and the classes are either words or sub word units such as phonemes. The most commonly
used feature extraction method, known as linear predictive coding (LPC), has been used first in this work.
Then the other two popular methods, namely, the Mel frequency cepstral coefficients (MFCC) and
perceptual linear prediction (PLP) have also been applied. These methods are based on the models of the
human auditory system. A detailed review of the implementation of these methods have been described
first. Then the steps of the implementation have been elaborated for the development of an automatic
speech recognition system (ASR) for Bangla speech.
Segmentation Words for Speech Synthesis in Persian Language Based On Silence
Abstract: In speech synthesis in text to speech systems, the words usually break to different parts and use from recorded sound of each part for play words. This paper use silent in word's pronunciation for better quality of speech. Most algorithms divide words to syllable and some of them divide words to phoneme, but This paper benefit from silent in intonation and divide words at silent region and then set equivalent sound of each parts whereupon joining the parts is trusty and speech quality being more smooth . this paper concern Persian language but extendable to another language. This method has been tested with MOS test and intelligibility, naturalness and fluidity are better.Keywords:TTS, SBS, Sillable, Diphone.
Title:Segmentation Words for Speech Synthesis in Persian Language Based On Silence
Author:Sohrab Hojjatkhah, Ali Jowharpour
International Journal of Recent Research in Mathematics Computer Science and Information Technology (IJRRMCSIT)
Paper Publications
PERFORMANCE ANALYSIS OF DIFFERENT ACOUSTIC FEATURES BASED ON LSTM FOR BANGLA ...
This document discusses performance analysis of different acoustic features for Bangla speech recognition using LSTM neural networks. It develops a Bangla speech corpus and extracts linear predictive coding (LPC), Mel frequency cepstral coefficients (MFCC), and perceptual linear prediction (PLP) acoustic features from the corpus. The features are then used to train LSTM models for Bangla speech recognition and their performance is evaluated based on sentence correct rates on test data sets consisting of male and female speakers.
Speech-Recognition.pptx
The document outlines a methodology for developing an automatic speech recognition system, including extracting features from speech signals, building acoustic models using tools like hidden Markov models and Gaussian mixture models, and recognizing speech patterns to convert spoken words to text. It discusses challenges in applying speech recognition to under-resourced tribal languages in India and the need to preserve indigenous languages and cultures. The proposed research aims to implement and evaluate speech recognition techniques for tribal languages to help document and promote endangered languages.
American Standard Sign Language Representation Using Speech Recognition
Abstract: For many deaf people, sign language is the principle means of communication. This increases the isolation of hearing impaired people. This paper presents a system prototype that is able to automatically recognize speech which helps to communicate more effectively with the hearing or speech impaired people. This system recognizes speech signal . Recognized spoken words are represented using American standard sign language via a robotic arm and also on the computer using visual basic .In this project a software package is provided to convert the speech signal, (which does not have any meaning for the deaf and the dumb) into the sign language. The main purpose of this project is to bridge the communication and expression gap between the normal people who cannot understand the sign language, and the deaf and dumb who cannot understand the normal speech.
An Extensible Multilingual Open Source Lemmatizer
This document summarizes an article that presents an open-source lemmatizer called GATE DictLemmatizer. The lemmatizer currently supports lemmatization for English, German, Italian, French, Dutch, and Spanish. It uses a combination of automatically generated lemma dictionaries from Wiktionary and the Helsinki Finite-State Transducer Technology (HFST). An evaluation shows it achieves similar or better results than the TreeTagger lemmatizer for languages with HFST support, and still provides satisfactory results for languages without HFST support. The lemmatizer and tools to generate dictionaries are made freely available as open source.
Announcing Amazon Polly - Turn Text into Lifelike Speech - December 2016 Mont...
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GROUP5-SYLLABLES
The document discusses the Syllable operating system project, which is developing two operating systems - Syllable Desktop and Syllable Server. Syllable Desktop is meant for personal computers and provides an easy to use graphical interface, while Syllable Server is meant for servers and provides command line access initially but will also get a graphical interface. Both systems aim to provide powerful capabilities while being easy to use.
Os Group5
The document discusses the Syllable operating system project, which is developing two operating systems - Syllable Desktop and Syllable Server. Syllable Desktop is meant for personal computers and provides an easy to use graphical interface, while Syllable Server is meant for servers and provides command line access initially but will also get a graphical interface. Both systems aim to provide powerful yet easy to use operating systems and work together, though Syllable Server is based on the Linux kernel while Syllable Desktop originated from a different system.
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Segmentation Words for Speech Synthesis in Persian Language Based On Silence
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Turkey Headscarf secular secularism kamal ataturk Islam secular muslim woman unfairness discrimination democracy military askr IIUM UIA
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Microsoft Word Hw#1
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Asr
Automatic speech recognition (ASR) systems convert spoken words to text. ASR systems have a speech database for training, extract acoustic features like MFCCs from speech, and use hidden Markov models trained on large datasets to recognize speech in real-time. ASR has applications in dictation, command and control, telephony, and assisting those with disabilities. The timeline of ASR shows steady improvements from isolated word recognition to today's systems that can understand continuous speech.
Microsoft Word The Project, Islam And Science
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Asr
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Sslis
- 1. By: Khalid El-Darymli G0327887 S peech to S ign L anguage I nterpreter S ystem ( SSLIS ) Supervisor: Dr. Othman O. Khalifa International Islamic University Malaysia Kulliyyah of Engineering, ECE Dept.
- 5. Main Parts of the SSLIS Speech-Recognition Engine Sign Language Database Recognized Text ASL Translation Continuous Input Speech Recognized Text
- 8. The Structure of SR Engine (LVCSR) Signal Processing AM P ( A 1 , …, A T | P 1 ,… , P k ) Dictionary P ( P 1 , P 2 , …, P k | W ) LM P ( W n | W 1 , …, W n-1 ) X={x 1 ,x 2 , …, x T } Hypothesis Evaluation Decoder P(X | W)*P(W) TRAINING DECODING Best Hypotheses H = {W 1 , W 2 , …, W k } W BEST Input Audio
- 17. ASL vs. SE (an Example) It is alright if you have a lot ASL Translation SE Translation IT I S ALL RIGHT IF YOU HAVE A LOT
- 18. DEMONSTRATION OF THE ASL IN OUR SW A number of 2,600 ASL prerecorded video clips In case of nonbasic word, extract the basic word out of it Recognized Word (SR engine’s output) Is the basic word within the ASL database vocabulary? The American Manual Alphabet Only in case of a nonbasic input word, append some suitable marker Final Output None of the database contents matched the input basic word No Yes Fingerspelling of the original input word The equivalent ASL video clip of the input word, some marker could be appended