Simulasi perbandingan performansi pengolahan text to speech menggunakan cool edit pro 2 dan Microsoft Speech Api (Sapi)

The evolving of information technology can stimulate another development in various fields, in which communications between computers with humans has been amended from time to time with the goal of communication between humans and computers can take place efficiently. TTS (Text to Speech) is the design of VUI (Voice Use Interface) application that developed from ASR (Automatic Speech Recognition) technology. Cool edit pro2 is a recording and voice processing application that has function as an auditing in a single wave (single waveform). Microsoft Speech API (SAPI) is a layer of software that is used by the pronouncing application to communicate with speech recognition. It aims to change text into sound form and to determine the pitch frequency and sampling using matlab. From each word and sentence recorded using cool edit, frequency pitch of 5 Hz - 70 Hz, sampling frequency obtained 3000 Hz – 25000 Hz. While using the Microsoft Speech API recording pitch frequency obtained 4.46 Hz - 75 Hz, sampling frequency 3000 Hz - 29000 Hz. From the analysis of the data obtained is better to use cool edit, because it does not use bit mono that have been set by database using engineer intonation

Development of a Real-time Embedded System for Speech Emotion Recognition

Speech emotion recognition is one of the latest challenges in speech processing and Human Computer Interaction (HCI) in order to address the operational needs in real world applications. Besides human facial expressions, speech has proven to be one of the most promising modalities for automatic human emotion recognition. Speech is a spontaneous medium of perceiving emotions which provides in-depth information related to different cognitive states of a human being. In this context, we introduce a novel approach using a combination of prosody features (i.e. pitch, energy, Zero crossing rate), quality features (i.e. Formant Frequencies, Spectral features etc.), derived features ((i.e.) Mel-Frequency Cepstral Coefficient (MFCC), Linear Predictive Coding Coefficients (LPCC)) and dynamic feature (Mel-Energy spectrum dynamic Coefficients (MEDC)) for robust automatic recognition of speaker’s emotional states. Multilevel SVM classifier is used for identification of seven discrete emotional states namely angry, disgust, fear, happy, neutral, sad and surprise in ‘Five native Assamese Languages’. The overall experimental results using MATLAB simulation revealed that the approach using combination of features achieved an average accuracy rate of 82.26% for speaker independent cases. Real time implementation of this algorithm is prepared on ARM CORTEX M3 board

Generative Adversarial Network with Convolutional Wavelet Packet Transforms for Automated Speaker Recognition and Classification

Speech is an effective mode of communication that always conveys abundant and pertinent information, such as the gender, accent, and other distinguishing characteristics of the speaker. These distinctive characteristics allow researchers to identify human voices using artificial intelligence (AI) techniques, which are useful for forensic voice verification, security and surveillance, electronic voice eavesdropping, mobile banking, and mobile purchasing. Deep learning (DL) and other advances in hardware have piqued the interest of researchers studying automatic speaker identification (SI). In recent years, Generative Adversarial Networks (GANs) have demonstrated exceptional ability in producing synthetic data and improving the performance of several machine learning tasks. The capacity of Convolutional Wavelet Packet Transform (CWPT) and Generative Adversarial Networks are combined in this paper to propose a novel way of enhancing the accuracy and robustness of Speaker Recognition and Classification systems. Audio signals are dissected using the Convolutional Wavelet Packet Transform into a multi-resolution, time-frequency representation that faithfully preserves local and global characteristics. The improved audio features better precisely describe speech traits and handle pitch, tone, and pronunciation variations that are frequent in speaker recognition tasks. Using GANs to create synthetic speech samples, our suggested method GAN-CWPT enriches the training data and broadens the dataset's diversity. The generator and discriminator components of the GAN architecture have been tweaked to produce realistic speech samples with attributes quite similar to genuine speaker utterances. The new dataset enhances the Speaker Recognition and Classification system's robustness and generalization, even in environments with little training data. We conduct extensive tests on standard speaker recognition datasets to determine how well our method works. The findings demonstrate that, compared to conventional methods, the GAN-CWPTs combination significantly improves speaker recognition, classification accuracy, and efficiency. Additionally, the suggested model GAN-CWPT exhibits stronger generalization on unknown speakers and excels even with loud and poor audio inputs

Acoustic cues to tonal contrasts in Mandarin: Implications for cochlear implants

The present study systematically manipulated three acoustic cues-fundamental frequency (f0), amplitude envelope, and duration-to investigate their contributions to tonal contrasts in Mandarin. Simplified stimuli with all possible combinations of these three cues were presented for identification to eight normal-hearing listeners, all native speakers of Mandarin from Taiwan. The f0 information was conveyed either by an f0-controlled sawtooth carrier or a modulated noise so as to compare the performance achievable by a clear indication of voice f0 and what is possible with purely temporal coding of f0. Tone recognition performance with explicit f0 was much better than that with any combination of other acoustic cues (consistently greater than 90% correct compared to 33%-65%; chance is 25%). In the absence of explicit f0, the temporal coding of f0 and amplitude envelope both contributed somewhat to tone recognition, while duration had only a marginal effect. Performance based on these secondary cues varied greatly across listeners. These results explain the relatively poor perception of tone in cochlear implant users, given that cochlear implants currently provide only weak cues to f0, so that users must rely upon the purely temporal (and secondary) features for the perception of tone. (c) 2008 Acoustical Society of America

Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624