Automatic Speech to Text Transcription

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Enhancing the Usability of Real Time Speech Recognition Captioning through Personalised Displays and Real Time Multiple Speaker Editing and Annotation

Enhancing the Usability of Real Time Speech Recognition Captioning through Personalised Displays and Real Time Multiple Speaker Editing and Annotation

Abstract. Text transcriptions of the spoken word can benefit deaf people and also anyone who needs to review what has been said (e.g. at lectures, presentations, meetings etc.) Real time captioning (i.e. creating a live verbatim transcript of what is being spoken) using phonetic keyboards can provide an accurate live transcription for deaf people but is often not available because of the cost and shortage of highly skilled and trained stenographers. This paper describes the development of a system that can provide an automatic text transcription of multiple speakers using speech recognition (SR), with the names of speakers identified in the transcription and corrections of SR errors made in real-time by a human ‘editor’.
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Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech

Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech

In this paper, our evaluation objects are the oral English picture compositions in English as a Sec- ond Language (ESL) examination. This examina- tion requires students to talk about four successive pictures with at least five sentences in one minute, and the beginning sentence is given. This examina- tion form combines both of the two forms described above. Therefore, we need two steps in the scoring task. The first step is Automatic Speech Recognition (ASR), in which we get the speech scoring features as well as the textual transcriptions of the speech- es. Then, the second step could grade the text-free transcription in an (conventional) AES system. The present work is mainly about the AES system un- der the certain situation as the examination grading criterion is more concerned about the integrated con- tent of the speech (the reason will be given in sub- section 3.1).
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An Automatic Real time Synchronization of Live speech with Its Transcription Approach

An Automatic Real time Synchronization of Live speech with Its Transcription Approach

Although many techniques are introduced for the automatic synchronization of text and audio at utterance levels, some applications require the automatic synchronization of text and audio in a finer level than an utterance level. This requirement has been discovered, while working on a project, namely ChulaDAISY [10]. ChulaDAISY is an application tool which aims to automatically generate audio books in DAISY 3 format [11] by gathering audio speech of volunteers who read transcriptions or contents in a book through the ChulaDAISY application. After the construction process of DAISY audio books has been finished, a user then can listen to audios and also will be able to navigate through an audio book reader, e.g. AMIS [12]. The audio book reader would play utterances together with highlight texts corresponding to those utterances because the binding between audios and texts are done at utterance level. In most languages, in particular English, an utterance is generally a sentence or a part of a sentence which is separated by spaces or punctuation marks. Therefore, the binding between audio and text at utterance does not cause any problem. However, the synchronization of text and audio in the utterance level is not suitable for Thai since sentences in Thai are constructed by consecutive words that are written continuously without any spaces or punctuation marks that could identify the end of sentences [12]. Even though white spaces could appear in some cases, they generally depend on decisions of writers to put them in order to emphasize or separate phases to match their desirable meaning. Consequently, in the audio book construction process, it is difficult for a user to read utterances without considering what they actually should read for each utterance. The workaround is to define the process for constructing DAISY audio books in Thai by giving users to prepare the transcriptions which they will read manually. The preparation of the transcription could be done by placing or removing white spaces and punctuation marks in suitable positions in order to separate or combine utterances before each utterance is read. According to our practical experience, the process for preparing transcriptions is time-consuming and methods of preparing transcription are varied by one user to another. Due to the nature of sentences in Thai that lack clear separation marks between sentences, the most appropriate approach to cope with the Thai nature problem is to take words or syllables level into account instead of considering utterances.
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Smart Reader for Visually Impaired People Using Raspberry Pi

Smart Reader for Visually Impaired People Using Raspberry Pi

ABSTRACT : Nowadays realtime hardware implementation of Text to Speech and Speech to text conversion systems playing a crucial role in several real time applications such as reading aid for blind people and talking aids for vocally handicapped people and robotics etc. This paper describes the design and implementation of a system which involves conversion of text information present in the image to speech information and conversion of speech information given by user into text information. In this context raspberry pi has been chosen as a hardware platform to implement the proposed method. For the implementation proposed system Logitech C170 camera module and Bluetooth HC-05 module were interfaced to raspberry pi device. The concept used in this project are tesseract OCR(Optical Character Recognition),espeak TTS(Text to Speech) engine,AMR(android meets robots) voice to text application software. The code which is used in the proposed system is used in the python programming language. The proposed system which is implemented on raspberry pi is used for many real time applications.
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An HMM Based Approach to Automatic Phrasing for Mandarin Text to Speech Synthesis

An HMM Based Approach to Automatic Phrasing for Mandarin Text to Speech Synthesis

Automatic phrasing is essential to Mandarin text- to-speech synthesis. We select word format as target linguistic feature and propose an HMM- based approach to this issue. Then we define four states of prosodic positions for each word when employing a discrete hidden Markov model. The approach achieves high accuracy of roughly 82%, which is very close to that from manual labeling. Our experimental results also demonstrate that this approach has advantages over those part-of- speech-based ones.

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SPEECH PROCESSING –AN OVERVIEW

SPEECH PROCESSING –AN OVERVIEW

One of the earliest goals of speech processing was coding speech for efficient transmission. Later, the research spread in various area like Automatic Speech Recognition (ASR), Speech Synthesis (TTS), Speech Enhancement, Automatic Language Translation (ALT).Initially, ASR is used to recognize single words in a small vocabulary, later many product was developed for continuous speech for large vocabulary .Speech Synthesis is used for synthesizing the speech corresponding to a given text Speech Synthesis provide a way to communicate for persons unable to speak. When Speech Synthesis used together with ASR, it allows a complete two-way spoken interaction between humans and machines. Speech Enhancement technique is applied to improve the quality of speech signal. Automatic Language Translation helps to convert one language into another language. Basic concept of speech processing is provided for beginners.
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A Framework for Combining Acoustic and Textual Features in Sentiment Analysis

A Framework for Combining Acoustic and Textual Features in Sentiment Analysis

In this paper, a method has been proposed that accepts a wave format audio clip to extracts emotion features and sentiment features. These extracted features used to predict sentiments of a customer speaking to a Call-Centre agent. The extracted three different feature sets are investigated by using well performing classifier. The notable contribution of this work lies in the analysis of the role of human emotions expressed in the form of speech for increasing the intensity of sentiment polarity. Also, it is demonstrated how emotion features can be combined with lexicon based sentiment features in machine learning based sentiment analysis technique. From the experimental results, it is evident that the human emotion signal increases the classification accuracy of sentiment categorization process. The finding of this qualitative study can be used in variety of applications such as automated customer behavior analysis, customer redress systems, customized retail services, and business process quality improvement.
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Robust Features for Automatic Text-Independent Emotion Recognition from Speech

Robust Features for Automatic Text-Independent Emotion Recognition from Speech

Verbalization emotion apperception is one of the latest challenges in verbalization processing and Human Computer Interaction (HCI) in order to address the operational needs in authentic world applications. Besides human visages, verbalization has proven to be one of the most promising modalities for automatic human emotion apperception. Verbalization is a spontaneous medium of perceiving emotions which provide in-depth information cognate to different cognitive states of a human being. In the verbal channel, the emotional content is largely conveyed as constant paralinguistic information signals, from which prosody is the most consequential component. The lack of evaluation of affect and emotional states in human machine interaction is, however, currently constraining the potential deportment and utilizer experience of technological contrivances. In this Paper, verbalization prosody and cognate acoustic features of verbalization are utilized for the apperception of emotion from verbalized Finnish. More categorically, methods for emotion apperception from verbalization relying on long-term ecumenical prosodic parameters are developed. An information fusion method is developed for short segment emotion apperception utilizing local prosodic features and vocal source features. A framework for emotional verbalization data visualization is presented for prosodic features.
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Cheap, Fast and Good Enough: Automatic Speech Recognition with Non Expert Transcription

Cheap, Fast and Good Enough: Automatic Speech Recognition with Non Expert Transcription

We first randomly selected one of the three transcriptions per utterance (as if the data were only tanscribed once) and repeated this three times with little variance. Selecting utterances randomly by Turker performed similarly. Per- formance of an LVCSR system trained on the non-professional transcription degrades by only 2.5% absolute (6% relative) despite a disagree- ment of 23%. This is without any quality control besides throwing out empty utterances. The degradation held constant as we swept the amount of training data frome one to twenty hours. Bot the acoustic and language models ex- hibited the log-linear relationship between WER and the amount of training data. Independent of the amount of training data, the acoustic model degraded by a nearly constant 1.7% and the lan- guage model by 0.8%.
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A SPEECH RECOGNITION AND SYNTHESIS TOOL

A SPEECH RECOGNITION AND SYNTHESIS TOOL

Many of the new technologies designed to help worldwide communication – e.g. telephones, fax machines, computers – have created new problems especially among the hearing and visually impaired. A person, who has severe hearing impairments, particularly to the extent in which deafness occurs, may experience difficulties communicating over a telephone as he or she is unable to hear the recipient’s responses. Conversely, someone with visual impairments would have little inconvenience using a telephone but may not be able to communicate through a computer because of the difficulties (or, in the case of blindness, impossibility) in reading the screen. The goal of this paper is to incorporate current speech recognition (speech-to-text) and speech synthesis (text-to-speech) technology into a chat room, thus, providing a solution to communication between the hearing and visually impaired that is free and does not require any additional equipment besides a computer.
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A Survey on Voice Based Mail System for Physically Impaired Peoples

A Survey on Voice Based Mail System for Physically Impaired Peoples

For a visually impaired person handling a computer who has never made use of it, becomes inconvenient comparatively normal user even it is user friendly. In order to overcome this trouble there are many screen readers are provided to user. A screen reader is computer program that enables a blind computer user to know what’s on the screen through speech. It read outs all contents present on screen but to perform any action person will have to make use of keyboard shortcuts because they enable to trace out the mouse locations. In short user have to know all key locations and have to remember key shortcuts [4] .
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Automatic Text Generation

Automatic Text Generation

AUTOMATIC TEXT GENERATION 1 0 INTRODUCTION Automatic text generation is the generation of natural language texts by computer It has applications in automatic documentation systems, automatic letter wr[.]

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A Review On Different Feature Recognition Techniques For Speech Process In Automatic Speech Recognition.

A Review On Different Feature Recognition Techniques For Speech Process In Automatic Speech Recognition.

Speech is the important way of communication. Speech processing is one of the most rousing research areas under signal processing. The signals are generally processed in digital domain; hence speech processing can also be distinctively called as digital signal processing appertained to speech signal. Automatic Speech Recognition (ASR) is a computer speech recognition system. It is a course of action of converting speech signal into series of words and other lingual units with help of algorithms which could be implemented as computer programs. The predominant objective of ASR is to develop different techniques and also system to enable the computers to identify speech signals which are fed as input. Speech recognition and its applications have evolved from past few decades. In any of the speech recognition system the speech signal is converted into text form out of which, the text form will be the output from ASR and this text will be almost equivalent to the speech fed as input. This recognition has its procesecution in voice search, voice dialling, robotics etc. most of the speech recognition systems are working on HMMs-Hidden Markov Models. The important aspect of HMM being extensively used in HMM is the parameters which can easily and automatically being erudite and trained. It is also computationally practical to use. Though many forge on have been made in field of ASR still we are impotent to develop machine which can understand all kinds of human speech in any environment. In this paper we discuss about different feature recognition techniques which will help in ASR.[1-3]
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Jurilinguistic Engineering in Cantonese Chinese: An N gram based Speech to Text Transcription System

Jurilinguistic Engineering in Cantonese Chinese: An N gram based Speech to Text Transcription System

After some initial trial tests, error analysis was conducted to investigate the causes of the mis- transcribed characters. It showed that a noticeable amount of errors were due to high failure rate in the retrieval of some characters in the transcription. The main reason is that high frequency characters are more likely to interfere with the correct retrieval of other relatively lower frequency homophonous characters. For example, Cantonese, hai (‘to be’) and hai (‘at’) are homophonous in terms of segmental makeup.

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Speech Translation System for Language Barrier Reduction

Speech Translation System for Language Barrier Reduction

Seung Yun, Young-Jik Lee, et al., [11] in 2014 proposed a work on Multilingual Speech-to-Speech Translation System for Mobile Consumer Devices. In this paper they established a massive language speech database closest to the environment where the speech-to-speech translation device is actually used after mobilize many people based on the user survey requests. It was possible to secure excellent basic performance in the environment similar to speech-to-speech translation environment rather than just under the experimental environment. Moreover, with the speech-to- speech translation interface, a user-friendly interface has been designed and at the same time the errors were reduced during the translation process so many steps to improve the user satisfaction were employed.
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A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

In second-generation synthesis systems, a unit (typi- cally a diphone) for each unique type was recorded. Pitch and timing of units were modified applying signal pro- cessing techniques to match the synthesis specification [1]. Some works exploited signal processing capabilities to generate singing from a spoken database. Flinger [9] for instance used residual LPC synthesis and provided sev- eral modules in order to enable the Festival TTS system [30] to sing. MBROLA was also used to generate both speech and singing from speech units [10, 31]. Similarly, the Ramcess synthesiser [32] generated singing by con- volving vocal tract impulse responses from a database with an interactive model of the glottal source. However, the data-driven paradigm of second generation synthesis systems naturally led to the creation of singing databases. Finally, it should be noted that there have been some recent attempts to produce singing from speech in a corpus-based TTS system. Some works used the system to get a spoken version of the song and transform it into singing by incorporating a signal processing stage. For instance, in [22], the synthetic speech was converted into singing according to a MIDI file input, using STRAIGHT to perform the analysis, transformation and synthesis. In [17], an HMM-based TTS synthesiser for Basque was used to generate a singing voice. The parameters provided by the TTS system for the spoken version of the lyrics were modified to adapt them to the requirements of the score. 2.2 Speech-to-singing
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Pragmasum: Automatic Text Summarizer Based On User Profile

Pragmasum: Automatic Text Summarizer Based On User Profile

original text; in 1969, Edmundson addresses the computational potential for transmitting meaning to the original text; in 1975, Pollock and Zamora reinforce the relevance of domain restriction; in 1987, Hutchins classifies summaries as indicative, informative, and critical; in 1993, Maybury suggests the use of a hybrid approach; in 1997, Marcu explores the rhetorical associations among sentences in the text; also in 1997, Hovy and Lin explore the use of symbolic knowledge and statistical techniques for summarization; in 1999, Sparck states that the Hutchins (1987) is a contingent factor for establishing its applicability and creating a consistent and McKeown (2012) and . (2007) recently conducted studies using er text characteristics, IDF term weighting, position of sentences, relation between title and signal phrases, etc. Other approaches consider semantic associations between words and combine them with similar characteristics in the process of sentence similarity. Examples of these approaches are, among and Liu (2001), topic (2001), and sentence grouping by Pardo (2016) propose nriching the summary using the text subject based on text segments. The main idea is that a text can be segmented into smaller ideas, or its subtopics, in order for each subtopic of the text to be represented by a text segment which is coherent with more sentences in the same line. According to (2015), during recent years, AS research for sets of documents have attracted greater interest in based approaches and based on topic Bayesian models. s incorporate the concept of gram language models. The research published in the AS field indicate two methodologies: the superficial method, which uses statistical processes, and the thorough method, which is composed of linguistic models. In addition to those methods, a hybrid approach is proposed that uses the former methods combined for AS. AS studies indicate two distinct categories for obtaining summaries: extractive summarization and abstractive summarization. According to and OLIVEIRA (2015), the extractive methods select a subset of words, phrases or sentences existing in the source text to compose the summary. Whereas the abstraction- based method creates a compact version by transmitting the
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Personality in Speech: Theories of Psychology, Questionnaires, Speech Databases

Personality in Speech: Theories of Psychology, Questionnaires, Speech Databases

The quality of any automated device is evaluated by the device. All devices currently used to read texts suffer from a lack of natural speech features, although it is understandable. Therefore, it is necessary to have an automatic generator of high quality sound in any generator system to speak, among most recent research and projects in this field, there is an increasing interest in machine training methods such as neural network, hidden Markov models and other probabilistic methods. Most of these methods are based on the verbal and empirical analysis of the recorded and classified voice. Due to the lack of work in this field for the Arabic language, this work provided the stages for the completion of a sound database of Syrian dialects such as those available for foreign languages such as TIMIT (English) and BDSONS
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Automatic Pronunciation Scoring And Mispronunciation Detection Using CMUSphinx

Automatic Pronunciation Scoring And Mispronunciation Detection Using CMUSphinx

Feedback on pronunciation is vital for spoken language teaching. Automatic pronuncia- tion evaluation and feedback can help non-native speakers to identify their errors, learn sounds and vocabulary, and improve their pronunciation performance. These evaluations commonly rely on automatic speech recognition, which could be performed using Sphinx trained on a database of native exemplar pronunciation and non-native examples of fre- quent mistakes. Adaptation techniques using target users' enrollment data would yield much better recognition of non-native speech. Pronunciation scores can be calculated for each phoneme, word, and phrase by means of Hidden Markov Model alignment with the phonemes of the expected text. In addition to the basic acoustic alignment scores, we have also adopted the edit distance based criterion to compare the scores of the spoken phrase with those of models for various mispronunciations and alternative correct pronunciations. These scores may be augmented with factors such as expected duration and relative pitch to achieve more accurate agreement with expert phoneticians' average manual subjective pronunciation scores. Such a system is built and documented using the CMU Sphinx3 sys- tem and an Adobe Flash microphone recording, HTML/JavaScript, and rtmplite/Python user interface.
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Automatic extraction of subcorpora based on subcategorization frames from a part of speech tagged corpus

Automatic extraction of subcorpora based on subcategorization frames from a part of speech tagged corpus

Automatic extraction of subcorpora based on subcategorization frames from a part of speech tagged corpus Automatic extraction of subcorpora based on subcategorization frames from a part of speech tagg[.]

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