![]() ![]() During the speech recognition process, a list of possible texts is created, and finally, the most relevant text to the original sound signal is selected. Finally, section six closes the review with final remarks and conclusions.Īn ASR application accepts the speech signal as input and converts it into a series of words in text form. Next, in section five, we analyze the data and the methodology we use for the experimental procedure. In section four, we describe criteria and metrics used for system evaluation. In section three, some of the most well-known ASR systems are introduced. In section two, we briefly present analyze the Architecture of ASR systems. The rest of this review is organized as follows. Therefore, we set the foundations for a more general assessment of most ASR systems with the ultimate goal of choosing the most appropriate vocabulary learning assistant. Thus, this work first attempts a presentation of known ASR systems and then proceeds to benchmark three well-known systems, namely IBM Watson, Google, and Wit. In this context, it is necessary to select the appropriate ASR. In particular, this assistant will be able to contribute to the learning of vocabulary by entering into dialogues with the trainee. Our immediate plans are to build an artificial vocabulary learning assistant. Īs the number of ASR systems is continuously increasing, it is quite challenging to select the most appropriate for a particular application. Also, ASR systems are utilized to provide more specialized services, such as training in pronunciation and vocabulary. Today, ASR applications are not just confined to human-machine communication for personal use but include industrial machine guidance with voice commands, automatic telephone communication, communication with automotive systems, military vehicles, and other equipment, communication with health care, aerospace and other systems. This user-friendly system is one of the first steps in improving human-machine communication. Later, in 1981, Logica developed a real-time speech recognition system based on the original project of the Joint Speech Research Unit in the United Kingdom. Vintsyuk presented an algorithm that can recognize speech, creating a sequence of words that contained in continuous and connected speech. Raj Reddy constructed the first recognition system of continuous speech as a student at Stanford University in the late 1960s. This early system required users to pause after each digit. ![]() Although man naturally acquires speech in early growth, speech production and recognition by computers is a complicated process that has extensively been addressed by the research community.Īs early as 1952, the first system was built that could identify digits with high precision. ASR refers to the conversion of speech into text, while TTS, as its name suggests, is the reverse process. ![]() ![]() Two primary technologies have developed concerning speech: the Automatic Speech Recognition or ASR, for short, and the Text to Speech or TTS. Therefore, acquiring speech from computers is reasonable to contribute to more effective man-machine communication. Speech is probably the primary means of man communication. ![]()
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