Literature Review On Speech Recognition

Speech is often based on concatenation of natural speech i.e units, that are taken from natural speech put together to form a word or sentence. Concatenative speech synthesis .has become very popular in recent years due to its improved

c. Isolate and pronounce initial, medial vowel, and final sounds (phonemes) in spoken single‐syllable words.(1.RF.2.c)

Speech and language services - Speech and language services help people all ages from babies to elderly people with their communication. They can provide any suggestions

Phonological awareness involves the detection and manipulation of sounds at three levels of sound structure: (1) syllables, (2) onsets and rimes, and (3) phonemes.

As the speaker continues more information is given. The listener is then able to collect the verbal and non-verbal data to gain a better understanding of what is being communicated (Eunson 2008: 310).

Communication and language - joins syllables such as mamma-bab-daaa, vocabulary 3-20 words, over extends words eg all round things are 'ball'

Speech and language services- they can help people who have problems with their speech or who has had a stroke and have difficulties with their speech.

This assignment was challenging because I do not watch television, go to movies, or listen to music. It is not that I am against these activities, but I have difficulty hearing varying tones and cannot easily discern words within recordings.

Word Recognition focuses strictly on a student’s ability to recognize written words quickly and effortlessly. Based off the grade level of my students, they should have the ability to go through a list of words rapidly pronouncing them correctly. The goal is to get them to be able to recognize and identify 10th grade leveled

The stimulus sizes (and critical feature sizes) calculated and used for the eccentricities 0˚, 20˚, 40˚ and 60˚ were 0.9cm (0.18cm), 2.2cm (0.44cm), 6.13cm (1.23cm) and 17.5cm (3.5cm) respectively.

Full phoneme segmentation: Counting out the number of syllables. Speech practice with CVC words. Have students participate in silent reading and use their own ability to sound out words they don’t know with syllable practice. Students can count the number of syllables in a rhyme or poem, they can clap together counting syllables in

When speaking to each other, we don’t pause between words. In other words, we use continuous speech. However, for speech recognition systems, there is difficulty in dealing with continuous speech (6, p.98). The easy way out will be using discrete speech where we pause between words (6, p.100). With discrete speech input, the silent gap between words is used to determine the boundary of the word, whereas in continuous speech, the speech recognition system must separate words using an algorithm which is not a hundred per cent accurate. Still, for a small vocabulary and using grammar, continuous speech recognition systems are available. They are reliable and do not require great computational power (6, p.100). However, for

Natural language systems interact allow computers to interact with the user in their usual language. They accept, interpret, and execute the commands in this language. The attempt is to allow a more natural interaction between the computer and user. Language is sometimes thought to be the foundation of intelligence in humans. Therefore, it is reasonable for intelligent systems to be able to understand language. Some of these systems are advanced enough to hold conversations.

Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to text. The term "voice recognition" is sometimes used to refer to recognition systems that must be trained to a particular speaker—as is the case for most desktop recognition software. Recognizing the speaker can simplify the task of translating speech.

Bangla word recognition is extremely challenging and there are too few works reported on online cursive Bangla word recognition. Bangla is a complicated script and it needs rigorous investigations to implement a better recognition system. While we have sophisticated classifiers like Hidden Markov Models or BLSTM Neural Networks for recognition of complicated scripts, we have very little comparative studies about the appropriate feature sets for such scripts. In this paper, our aim is to implement an appropriate recognition system for author-independent unconstrained Bangla word where a modified feature set is proposed. To construct the modified feature set, we modify existing feature sets and add new features for improvement of recognition accuracy. We test performances of various existing feature sets and proposed feature set on same Bangla dataset for fair comparison and report the comparative results using various lexicons upto 20,000-word lexicon. Each time an HMM-based classifier is used to test the feature set. Finally, a recognition system is built over the combination of existing and modified feature sets.