The details of the new Romanian text processor we have developed are also given.Using the database, we then revisit some basic configuration choices of speech synthesis, such as waveform sampling frequency and auditory frequency warping scale, with the aim of improving speaker similarity, which is an acknowledged weakness of current HMM-based speech synthesisers. The RSS corpus comprises 3500 training sentences and 500 test sentences uttered by a female speaker and was recorded using multiple microphones at 96 kHz sampling frequency in a hemianechoic chamber. All of these are now freely available for academic use in order to promote Romanian speech technology research. This paper first introduces a newly-recorded high quality Romanian speech corpus designed for speech synthesis, called “RSS”, along with Romanian front-end text processing modules and HMM-based synthetic voices built from the corpus. The application achieved an accuracy 84,85%. Acceptable performance results are observed when the application is evaluated using word error rate for intelligibility, and subjective mean opinion score for pronunciation, naturalness, pleasantness, understandability, and overall system impression. The application front-end component parses mathematical expression text inputs before a TTS synthesis system processes them to produce the correct articulation of the mathematical expression. This paper presents the development of a grammar-driven TTS application for the reading of mathematical expressions in the Sepedi language. Spoken languages plays a vital role to the educational journey of children as their brains are naturally wired to speak but not read and write. The TTS synthesis systems assist with the correct word spelling and intonation. Grammar-based applications tend to be effective when embedded within text-to-speech (TTS) synthesis systems. One of the major requirements in processing speech synthesis tasks is the correctness of grammar analysis.
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Natural Language Processing (NLP) forms one of the important and fundamental components of speech synthesis while a language grammar forms one of the important requirements for NLP tasks. The system is available as a website service. The quality of the voices is measured using the mean opinion score and word error rate metrics that resulted with positive results on the understandability, naturalness, pleasantness, intelligibility and overall impression of the system of the newly created TTS voices. A robust method for building TTS voices called hidden Markov model method is used to build new voices in the selected languages. The LID module is trained on a 4 million words dataset resulted with 99% accuracy outperforming the state-of-the-art On the front-end, is the LID module that detects language of the input text before the TTS synthesis module produces output audio. This paper presents the development of a multi-language LID+TTS synthesis system that generate audio of input text using the predicted language in four South African languages, namely: Tshivenda, Sepedi, Xitsonga and IsiNdebele. Mitigating the historical linguistic effects of discrimination and domination imposed onto low-resourced indigenous languages. Development of language-specific systems like TTS and Language identification (LID) have an important task to address in Scarcity of these systems may lead to challenges in learning new languages specifically low-resourced languages. These systems are currently available for various major languages but not available for low-resourced languages. Text-to-speech (TTS) synthesis systems are of benefit towards learning new or foreign languages.
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#Cepstral voices british download software
The created voices will be very useful for incorporation into voice enabled software applications that are targeted at additional English speakers. The speech data will be recorded from male and female South Africans who speak English as their additional language. We will use existing sentences for recording the speech training data.
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We are going to adopt an existing speech synthesis toolkit, The Hidden Markov Model (HMM)-based speech synthesis system (HTS) engine in creating our voices. The text-to-speech (TTS) synthesis system to be developed will be focusing on English as spoken by South Africans who are additional language speakers of English. The quality of voices created will be measured in terms of their intelligibility, similarity and naturalness. In this paper, we present an approach of creating English voices as spoken by additional language speakers that can be used to facilitate such learning. Speech synthesis systems can be used for language learning by additional language speakers.