Design and Implementation of Speech Based Scientific Calculator
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In recent days speech recognition based systems are gaining more prominence and are used in a wide range of applications. Calculator has become an indispensable part of our lives, used at home, at local retail shops or at workplaces like banks, IT companies, medical laboratories etc. Speech recognized calculator is one of the speech recognition systems, in which the digits and operators spoken by the user with pauses in between are analyzed using various algorithms and finally one of the 10 digits (0 to 9) and one of the 5 operators(+, -, *, /, =) The objective of our paper is to design and implement the speech recognized calculator in MATLAB and Standalone Microcontroller. This designed system is able to take detect the digits and operators of the arithmetic expression whose result is to be calculated. In this paper we have used linear predictive method for recognizing the digit/operator in MATLAB and Analog Speech Recognition Chip.
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This document discusses speech feature extraction and classification techniques for speech recognition systems. It provides an overview of common feature extraction methods like MFCC and LPC, and classification algorithms like ANN and SVM. MFCC mimics human auditory perception but provides weak power spectrum, while LPC is easy to calculate but does not capture information at a linear scale. ANN can learn from data but is complex for large datasets, while SVM is accurate and suitable for pattern recognition but requires fixed-length coefficients. The document evaluates these techniques and concludes that MFCC performance is more efficient than LPC for feature extraction in speech recognition.
Speaker recognition using MFCC
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
Utterance based speaker identification
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
G010424248
This document analyzes speech coding algorithms for Hindi and English languages. It discusses Linear Predictive Coding (LPC), an algorithm that accurately estimates speech parameters and represents speech signals at reduced bit rates while preserving quality. The paper proposes a voice-excited LPC algorithm and implements it on Hindi and English male and female voices. It analyzes tradeoffs between bit rates, delay, signal-to-noise ratio, and complexity. The results show low bit-rates and better signal-to-noise ratio with this algorithm.
Utterance Based Speaker Identification Using ANN
This document summarizes a research paper on speaker identification using artificial neural networks. The paper presents a speaker identification system that uses digital signal processing and ANN techniques. Speech features are extracted from utterances using FFT and windowing. These features are used to train a multi-layer perceptron network to classify speakers. The system was tested on Bangla speech and achieved accurate identification of speakers from their utterances.
Utterance Based Speaker Identification Using ANN
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using back propagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This document summarizes research on using linear predictive coding (LPC) and related techniques for speech recognition and compression. Key points discussed include:
1) LPC is used to compress and encode speech signals for transmission by determining a filter to predict samples from past values, minimizing error. Filter coefficients are encoded and decoded.
2) LPC and PARCOR parameters can characterize phonemes and have potential for speech recognition by analyzing short frames of speech. Recognition rates of 65% for vowels and 94% for consonants were achieved.
3) An LPC-based speech coding system was implemented and tested for mobile radio communications, achieving a bit error rate performance suitable for speech transmission.
Course report-islam-taharimul (1)
This document provides a course report on speech recognition using MATLAB. It includes an abstract, introduction, report outline, design of the system, process and results, flowchart of the experiment, simulation and analysis, discussion, and conclusion sections. The report details using MFCC and GMM algorithms for speech recognition, including signal preprocessing, parameter extraction, and training. Simulation results showed recognition rates approaching 100% for same speakers in quiet environments, but performance was impacted by noise and different speakers. The system demonstrated speech recognition capabilities in MATLAB but had limitations.
Speaker Recognition System using MFCC and Vector Quantization Approach
This paper presents an approach to speaker recognition using frequency spectral information with Mel frequency for the improvement of speech feature representation in a Vector Quantization codebook based recognition approach. The Mel frequency approach extracts the features of the speech signal to get the training and testing vectors. The VQ Codebook approach uses training vectors to form clusters and recognize accurately with the help of LBG algorithm.
Speaker Recognition Using Vocal Tract Features
Speaker Recognition Using Vocal Tract FeaturesInternational Journal of Engineering Inventions www.ijeijournal.com
This document summarizes a research paper on speaker recognition using vocal tract features. It discusses extracting pitch using FFT as a vocal source feature and Mel Frequency Cepstral Coefficients (MFCCs) as vocal tract features. These features are used as inputs to a Support Vector Machine (SVM) classifier for binary speaker classification. Experimental results show the SVM achieves up to 94.42% accuracy in classifying speakers based on their MFCC features, and accuracy increases with higher signal-to-noise ratios when noise is added to speech signals. The paper concludes vocal source and tract features can be used together with SVM for text-independent speaker recognition.[IJET-V1I6P21] Authors : Easwari.N , Ponmuthuramalingam.P
[IJET-V1I6P21] Authors : Easwari.N , Ponmuthuramalingam.PIJET - International Journal of Engineering and Techniques
This document discusses feature extraction techniques for isolated word speech recognition. It begins with an introduction to digital speech processing and speech recognition models. The main part of the document compares two common feature extraction techniques: Mel Frequency Cepstral Coefficients (MFCC) and Relative Spectral (RASTA) filtering. MFCC allows signals to extract feature vectors and provides high performance but lacks robustness. RASTA filtering reduces the impact of noise in signals and provides high robustness by band-passing feature coefficients in both log spectral and spectral domains. The document provides details on the process of MFCC feature extraction, which involves steps like framing, windowing, fast Fourier transform, mel filtering, discrete cosine transform, and calculatingIjetcas14 426
This document presents a text-dependent speaker recognition system using neural networks that aims to improve recognition accuracy. It proposes changing the number of Mel Frequency Cepstral Coefficients (MFCCs) used in training. Voice Activity Detection is also used as a preprocessing step. Experimental results show recognition accuracy increases from 70.41% to a maximum of 92.91% as the number of MFCCs increases from 14 to 20, but then decreases with more MFCCs. The system is implemented on a Raspberry Pi for hardware acceleration.
Automatic Sound Signals Quality Estimation
Benefits of easy integration of a new voice quality measurement software providing proof of concenpt and test results. Describes how easy integration process can be, advantages comparing with PESQ providing explicit test results.
Automatic Sound Signals Quality Estimation
This document discusses automatic sound signal quality estimation. It describes the basic components of an estimation system, including test signals, synchronization, and analytical modules. It also reviews existing quality estimation methods like AI, SII, STI, and C50. The proposed system generates test signals, synchronizes original and processed signals, and analyzes similarity to provide objective quality metrics. Potential applications include estimating quality of voicemail transmission, IP calls, and cellular/satellite connections. Further development areas are also outlined.
final ppt BATCH 3.pptx
This document presents a system for real time voice cloning using deep learning models. It discusses existing text-to-speech systems that are typically single speaker and proposes a system that can generate speech for different target speakers without observing them during training. The proposed system uses a speaker encoder to analyze a reference voice note, a synthesizer to generate text with the reference frequency, and a neural vocoder to produce a speech waveform. The document outlines the system architecture, requirements and concludes the goal of building a voice cloning system with data efficient natural speech generation for various speakers was achieved.
19 ijcse-01227
This document discusses speaker recognition techniques and applications. It provides an overview of common feature extraction methods for speaker recognition systems such as Linear Predictive Coding (LPC), Perceptual Linear Predictive Coefficients (PLPC), Mel Frequency Cepstral Coefficients (MFCC), and Gamma Tone Frequency Cepstral Coefficients (GFCC). It compares these techniques and finds that MFCC and PLPC are most often used due to being derived from models of human auditory processing. GFCC has benefits of higher frequency resolution and noise robustness compared to MFCC. Speaker recognition has applications in security systems for access control.
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Design and Implementation of Speech Based Scientific Calculator
- 1. Design and Implementation of Speech Based Scientific Calculator ABSTRACT IN RECENT DAYS SPEECH RECOGNITION BASED SYSTEMS ARE GAINING MORE PROMINENCE AND ARE USED IN AWIDE RANGE OF APPLICATIONS. CALCULATOR HAS BECOME AN INDISPENSABLE PART OF OUR LIVES, USED AT HOME, AT LOCAL RETAIL SHOPS OR ATWORKPLACES LIKE BANKS, IT COMPANIES, MEDICAL LABORATORIES ETC. SPEECH RECOGNIZED CALCULATOR IS ONE OFTHE SPEECH RECOGNITION SYSTEMS
- 2. Introduction • Speech is the vocalized form of human communication. It ranges from 90 Hz to 7,000 Hz. Each spoken word is created out of the phonetic combination of a limited set of vowel and consonant speech sound units. • Speech recognition is the process of automatic extracting and determining linguistic information conveyed by a speech wave using computers. This Speech recognized calculator can be implemented by using linear predictive coding (LPC) method. • Implementation of this system includes two stages namely a) Training phase b) Testing phase
- 3. What is Linear Predictive Coding(LPC)..? • Its one of the most important speech analysis techniques. • The basis of LPC model is that, for a given speech sample at time n, s(n), can be approximated as a linear combination of the past p speech samples, such that s˜(n) ≈ a1s(n − 1)+ a2s(n − 2)+ aps(n − p) • where the coefficients a1, a2,.... ap are assumed to be constant over the speech analysis frame in the equation represents the order of the linear predictor. • LPC can be implemented using either Auto Correlation Method, Covariance Method or Lattice Method
- 4. Block of MATLAB based SRC • 1. Speech Acquisition • 2. Speech Extraction • 3. Speech Recognition • 4. Pattern Training and Decision Logic
- 5. SPEECH ACQUISITION • Speech acquisition refers to the process of acquiring the required speech input from the user. • During speech acquisition, speech samples are obtained from the speaker in real time and stored in memory for preprocessing. • As the articulation bandwidth of voice is 300 Hz to 3 kHz. According to the Nyquist Sampling Theorem ,the sampling frequency is 8kHz as the minimum sampling rate. • In our project we have chosen the sampling rate to be 10 kHz so that more samples can be obtained which will be a greater advantage in further processing.
- 6. SPEECH EXTRACTION • Now recorded speech interval is fed to the extraction stage where the uttered digit or the operator is extracted from the entire recording interval. • The recording interval consists of voiced segments and unvoiced segments. • Below fig shows the extracted information due to sampling of recorded information in this stage
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