Comparing ANN to HMM in implementing limited Arabic vocabulary ASR systems

Alotaibi, Yousef Ajami

doi:10.1007/s10772-011-9107-3

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Additionally, there are various works on Arabic/Dialect/Amazigh speech recognition with different approaches, such as [17], [18], [19], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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In this study, we introduce an experimental framework for Moroccan Dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten most commonly used greetings in the Moroccan dialect, extracted from telephone conversations. This corpus was recorded with 60 speakers (30 males and 30 females). Each speaker articulated each expression three times in natural and noisy conditions. Feature extraction utilized Mel Scale Cepstral Coefficients (MFCC), and acoustic modeling, based on monophony, was implemented using Hidden Markov Models (HMM). While automatic speech recognition systems demonstrate commendable performance in noise-free conditions, their efficacy noticeably diminishes in the presence of noise.

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“…Additionally, there are various works on Arabic/Dialect/Amazigh speech recognition with different approaches, such as [17], [18], [19], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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“…In a more complicated case, if there is a possibility of invalid sequences in a command in the form of a pair of words, it is possible to use a matrix that considers only valid combinations of words and generates an error message otherwise. Such a simplified approach will eliminate the use of often used in speech and command recognition, but more complex hidden Markov models (HMM) [15,16], which require higher speed and longer computation time. When using HMM, the speech signal is represented as a probabilistic process taking into account time-varying [17].…”

Section: Resultsmentioning

confidence: 99%

Expanding functionality of the autonomous voice control system

Zhigalov

2023

E3S Web of Conf.

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The article describes the main advantages of voice control systems, notes the shortcomings of the first implemented voice control systems, their functional limitations, and limited use in comparison with modern voice assistants, and also formulates the shortcomings of modern voice control systems. It is noted that with the expansion of functionality, the scope of voice control systems is expanding significantly. The recommendations of leading companies that implement voice technologies, indicating the need to continue research and development in this area. Due to complex calculations in speech recognition, there is a need to use high-speed computers, which makes it difficult to create compact and autonomous voice control systems that work without an Internet connection. At the same time, the growing need for autonomous voice control systems requires a reduction in computational costs, an increase in the speed of calculations by simplifying the methods and algorithms of these systems, implemented in software and hardware in the form of autonomous electronic devices. Methods are distinguished that allow performing recognition with sufficient reliability, such as hidden Markov models and the method for determining mel-frequency cepstral coefficients. In addition, methods are proposed to reduce computational complexity by reducing the number of similar-sounding words included in the command and the commands themselves, and to increase the speed of calculations, the use of electronic components is proposed in the implementation of computational algorithms and their parts. The operability and the possibility of practical application of ways to expand functionality and reduce computational complexity for autonomous voice control systems have been tested and confirmed experimentally, and some of them correspond to the recommendations of leading companies implementing voice technologies.

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“…Similar results were obtained by researchers for other languages. Alotaibi (2012) obtained recognition results of vowels at the level of 92.13 % for Arabic (using lpc and artificial neural networks). Koulagudi et al (2012) obtained recognition results of vowels at the level of 91.4 % for Hindi (using MFCC).…”

Section: Comparison With Other Research Resultsmentioning

confidence: 99%

Using bands of frequencies for vowel recognition for Polish language

Płonkowski

2014

Int J Speech Technol

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This paper presents a simple and useful method of vowel recognition of the Polish language. It relies on determining some characteristic bands of frequencies for each vowel. These bands are chosen so as to provide maximum separability of all vowels. Within each band we determine three parameters: average, a standard deviation and a maximum value. Comparing these values with the previously designated boundary values, we can classify a given vowel. As shown by the test, this method has a low percentage of an incorrectly recognized vowel. An additional advantage is its efficiency. It is four times faster than the method based on the formants.

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Comparing ANN to HMM in implementing limited Arabic vocabulary ASR systems

Cited by 10 publications

References 12 publications

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

Expanding functionality of the autonomous voice control system

Using bands of frequencies for vowel recognition for Polish language

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