This report explores the subject of speech synthesis. Information given includes a brief explanation of speech production in man, an historical view of speech synthesis, and four types of electronic synthesizers in use today. Also included is a brief presentation on phonetics, the study of speech sounds. An understanding of this subject is necessary to see how a synthesizer must produce certain sounds, and how these sounds are put together to create words. Finally a description of a limited text speech synthesizer is presented. This system allows the user to enter English text via a keyboard and have it output in spoken form. The future of speech synthesis appears to be very bright. This report also gives some possible applications of verbal computer communication.

No description available.

This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process. / Master of Science

Recovering clean speech from an audio signal with additive noise is a problem that has plagued the signal processing community for decades. One promising technique currently being utilized in speech-coding applications is a multi-sensor approach, in which a microphone is used in conjunction with optical, mechanical, and electrical non-acoustic speech sensors to provide greater versatility in signal processing algorithms. One such non-acoustic glottal waveform sensor is the Tuned Electromagnetic Resonator Collar (TERC) sensor, first developed in [BLP+02]. The sensor is based on Magnetic Resonance Imaging (MRI) concepts, and is designed to detect small changes in capacitance caused by changes to the state of the vocal cords - the glottal waveform. Although preliminary simulations in [BLP+02] have validated the basic theory governing the TERC sensor's operation, results from human subject testing are necessary to accurately characterize the sensor's performance in practice. To this end, a system was designed and developed to provide real-time audio recordings from the sensor while attached to a human test subject. From these recordings, executed in a variety of acoustic noise environments, the practical functionality of the TERC sensor was demonstrated. The sensor in its current evolution is able to detect a periodic waveform during voiced speech, with two clear harmonics and a fundamental frequency equal to that of the speech it is detecting. This waveform is representative of the glottal waveform, with little or no articulation as initially hypothesized. Though statistically significant conclusions about the sensor's immunity to environmental noise are difficult to draw, the results suggest that the TERC sensor is considerably more resistant to the effects of noise than typical acoustic sensors, making it a valuable addition to the multi-sensor speech processing approach.

Thesis (M.Sc. (Computer Science)) -- University of Limpopo, 2012 / Automatic Speech Recognition (ASR) is a technology that allows a computer to identify spoken words and translate those spoken words into text. Speech recognition systems have started to be used in may application areas such as healthcare, automobile, e-commerce, military, and others. The use of these speech recognition systems is usually limited by their poor performance. In this research we are looking at improving the performance of the baseline ASR systems by incorporating syntactic structures in grammar into an existing Northern Sotho ASR, based on hidden Markov models (HMMs). The syntactic structures will be applied to the vocabulary used within the healthcare application area domain. The Backus Naur Form (BNF) and the Extended Backus Naur Form (EBNF) was used to specify the grammar. The experimental results show the overall improvement to the baseline ASR System and hence give a basis for following this approach.

No description available.

Thesis--PlanB (M.S.)--University of Wisconsin--Stout, 1998. / Includes bibliographical references.

This thesis examines the processing of accented speech in both infants and adults. Accents provide a natural and reasonably consistent form of inter-speaker variation in the speech signal, but it is not yet clear exactly what processes are used to normalise this form of variation, or when and how those processes develop. Two adult studies use ERP data to examine differences between the online processing of regional- and foreign-accented speech as compared to a baseline consisting of the listeners’ home accent. These studies demonstrate that the two types of accents recruit normalisation processes which are qualitatively, and not just quantitatively, different. This provided support for the hypothesis that foreign and regional accents require different mechanisms to normalise accent-based variation (Adank et al., 2009, Floccia et al., 2009), rather than for the hypothesis that different types of accents are normalised according to their perceptual distance from the listener’s own accent (Clarke & Garrett, 2004). They also provide support for the Abstract entry approach to lexical storage of variant forms, which suggests that variant forms undergo a process of prelexical normalisation, allowing access to a canonical lexical entry (Pallier et al., 2001), rather than for the Exemplar-based approach, which suggests that variant word-forms are individually represented in the lexicon (Johnson, 1997). Two further studies examined how infants segment words from continuous speech when presented with accented speakers. The first of these includes a set of behavioural experiments, which highlight some methodological issues in the existing literature and offer some potential explanations for conflicting evidence about the age at which infants are able to segment speech. The second uses ERP data to investigate segmentation within and across accents, and provides neurophysiological evidence that 11-month-olds are able to distinguish newly-segmented words at the auditory level even within a foreign accent, or across accents, but that they are more able to treat new word-forms as word-like in a familiar accent than a foreign accent.

A new algorithm is presented for real-time fundamental frequency estimation of speech signals. This method extends and alters the YIN algorithm, which uses the autocorrelation-based difference function, by adding features to reduce latency, correct predictable errors, and make it structurally appropriate for real-time processing scenarios. The algorithm is shown to reduce the error rate of its predecessor while demonstrating latencies sufficient for real-time processing. The results indicate that the algorithm can be realized as a real-time estimator of spoken pitch and pitch variation, which has applications including diagnosis and biofeedback-based therapy of many speech disorders.

胡承慈. / 大字複印本. / Thesis (M.A.)--香港中文大學研究院電子計算學部. / Da zi fu yin ben. / Includes bibliographical references (leaves 26-27 (2nd group)). / Hu Chengci. / Thesis (M.A.)--Xianggang Zhong wen da xue yan jiu yuan dian zi ji suan xue bu. / 嗚謝 --- p.I / ABSTRACT --- p.II / 提要 --- p.III / Chapter 第一章 --- 緒論 / Chapter 1.1 --- 用聲音作爲輸出媒介 --- p.1 / Chapter 1.2 --- 各種聲音輸出方法 --- p.1 / Chapter 1.3 --- 音素合成法及VOTRAX SC01語音合成片 --- p.3 / Chapter 1.4 --- 國語輸出 --- p.3 / Chapter 第二章 --- 中文分折 / Chapter 2.1 --- 國語分析 --- p.4 / Chapter 2.1.1 --- 國語音素 --- p.4 / Chapter 2.1.1.1 --- 單元音 --- p.4 / Chapter 2.1.1.2 --- 輔音 --- p.5 / Chapter 2.1.2 --- 國語音素的互相結合 / Chapter 2.1.2.1 --- 複元音 --- p.6 / Chapter 2.1.2.2 --- 鼻元音 --- p.6 / Chapter 2.1.3 --- 國語音節 --- p.7 / Chapter 2.2 --- 漢詞 --- p.9 / Chapter 2.3 --- 漢字 --- p.9 / Chapter 第三章 --- 硬件和軟件的設計 / Chapter 3.1 --- 硬件設計 --- p.10 / Chapter 3.2 --- 軟件設計 --- p.11 / Chapter 3.2.1 --- 漢字編碼和音素地址表 --- p.12 / Chapter 3.2.2 --- 音素串表 --- p.13 / Chapter 3.2.3 --- 語音合成器操作程序 --- p.14 / Chapter 3.2.4 --- 語音合成器管理程序和音素編輯程序 --- p.15 / Chapter 第四章 --- 建立和發現 / Chapter 4.1 --- 硬件的建立 --- p.15 / Chapter 4.2 --- 軟件的建立 --- p.18 / Chapter 4.2.1 --- 表的建立 --- p.18 / Chapter 4.2.2 --- 程序的建立 --- p.23 / Chapter 第五章 --- 結論 --- p.24 / Chapter 附錄A --- 參考資料 --- p.26 / Chapter 附錄B --- 漢語輔音音素表 --- p.28 / Chapter 附錄C --- 漢語元音音素表 --- p.29 / Chapter 附錄D --- SC01音素表 --- p.31 / Chapter 附錄E --- 軟件應用 / Chapter E.1 --- 語音合成器操作程序應用 --- p.34 / Chapter E.2 --- 語音合成器管理程序應用 --- p.41 / Chapter E.3 --- 音素編輯程序應用 --- p.45 / Chapter 附錄F --- 中英名詞對照表 / Chapter F.1 --- 聲音及發聲方法 --- p.53 / Chapter "F,2" --- 硬件 --- p.53 / Chapter F.3 --- 軟件 --- p.53 / Chapter 附錄G --- 語音合成器硬件電路圖 --- p.55