Towards a Knowledge Graph based Speech Interface

Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognised text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. These semantic annotations of recognised words can be represented as a subject-predicate-object triples which collectively form a graph often referred to as a knowledge graph. This type of knowledge representation facilitates to use speech interfaces with any spoken input application, since the information is represented in logical, semantic form, retrieving and storing can be followed using any web standard query languages. In this work, we develop a methodology for linking speech input to knowledge graphs and study the impact of recognition errors in the overall process. We show that for a corpus with lower WER, the annotation and linking of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight, a tool to interlink text documents with the linked open data is used to link the speech recognition output to the DBpedia knowledge graph. Such a knowledge-based speech recognition interface is useful for applications such as question answering or spoken dialog systems.Comment: Under Review in International Workshop on Grounding Language Understanding, Satellite of Interspeech 201

Parallel Algorithms for Isolated and Connected Word Recognition

For years researchers have worked toward finding a way to allow people to talk to machines in the same manner a person communicates to another person. This verbal man to machine interface, called speech recognition, can be grouped into three types: isolated word recognition, connected word recognition, and continuous speech recognition. Isolated word recognizers recognize single words with distinctive pauses before and after them. Continuous speech recognizers recognize speech spoken as one person speaks to another, continuously without pauses. Connected word recognition is an extension of isolated word recognition which recognizes groups of words spoken continuously. A group of words must have distinctive pauses before and after it, and the number of words in a group is limited to some small value (typically less than six). If these types of recognition systems are to be successful in the real world, they must be speaker independent and support a large vocabulary. They also must be able to recognize the speech input accurately and in real time. Currently there is no system which can meet all of these criteria because a vast amount of computations are needed. This report examines the use of parallel processing to reduce the computation time for speech recognition. Two different types of parallel architectures are considered here, the Single Instruction stream - Multiple Data (S1MD) machine and the VLSI processor array. The SIMD machine is chosen for its flexibility, which makes it a good candidate for testing new speech recognition algorithms. The VLSI processor array is selected as being good for a dedicated recognition system because of its simple processors and fixed interconnections. This report involves designing SIMD systems and VLSI processor arrays for both isolated and connected word recognition systems. These architectures are evaluated and contrasted in terms of the number of processors needed, the interprocessor connections required, and the “power” each processor needs to achieve real time recognition. The results show that an SIMD machine using 100 processors, each with an MC68000 processor, can recognize isolated words in real time using a 20 KHz sampling rate and a 1,000 word vocabulary

Problem spotting in human-machine interaction

In human-human communication, dialogue participants are con-tinuously sending and receiving signals on the status of the inform-ation being exchanged. We claim that if spoken dialogue systems were able to detect such cues and change their strategy accordingly, the interaction between user and systemwould improve. Therefore, the goals of the present study are as follows: (i) to find out which positive and negative cues people actually use in human-machine interaction in response to explicit and implicit verification questions and (ii) to see which (combinations of) cues have the best predictive potential for spotting the presence or absence of problems. It was found that subjects systematically use negative/marked cues (more words, marked word order, more repetitions and corrections, less new information etc.) when there are communication problems. Using precision and recall matrices it was found that various combinations of cues are accurate problem spotters. This kind of information may turn out to be highly relevant for spoken dia-logue systems, e.g., by providing quantitative criteria for changing the dialogue strategy or speech recognition engine

SISTEM KONVERSI UCAPAN KATA KE TEKS MENGGUNAKAN SUPPORT VECTOR MACHINE : SPEECH WORD RECOGNITION TO TEXT CONVERTER USING SUPPORT VECTOR MACHINE

Artificial intelligence technology is developing very rapidly. Various fields have applied this technology to help human work. Speech recognition system is one of the artificial intelligence technologies that are widely applied in various fields. However, some research showed that it was still necessary to develop a method for a good speech recognition system. In addition, the development of speech recognition systems that can provide benefits needs to be developed, such as text recording. Based on this, the research focuses on developing a speech recognition system, in the form of spoken words and convert to text form. Speech words that have been recorded are then extracted features using linear predictive coding method. After that, the characteristic features of each sound are trained and tested using the Support Vector Machine (SVM) method for the process of recognition and convert it into text. Based on the evaluation results show that this system is able to recognize words with an accuracy rate of 71.875%. These percentages indicate that the system is able to recognize spoken words and transform them into text form properly

Assessment of Classifiers for Potential Voice-Enabled Transportation Apps

Transportation apps are playing a positive role for today’s technology-driven users. They provide users with a convenient and flexible tool to access transportation data and services, as well as collect and manage data. In many of these apps, such as Google Maps, their operations rely on the effectiveness of the voice recognition system. For the existing and new apps to be truly effective, the built-in voice recognition system needs to be robust (i.e., being able to recognize words spoken in different pitch and tone). The goal of this study is to assess three post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy) to enhance the commonly used Google’s voice recognition system. The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that post-processing techniques could significantly enhance Google’s voice recognition system

Exploiting multiple ASR outputs for a spoken language understanding task

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-01931-4_19In this paper, we present an approach to Spoken Language Understanding, where the input to the semantic decoding process is a composition of multiple hypotheses provided by the Automatic Speech Recognition module. This way, the semantic constraints can be applied not only to a unique hypothesis, but also to other hypotheses that could represent a better recognition of the utterance. To do this, we have developed an algorithm to combine multiple sentences into a weighted graph of words, which is the input to the semantic decoding process. It has also been necessary to develop a specific algorithm to process these graphs of words according to the statistical models that represent the semantics of the task. This approach has been evaluated in a SLU task in Spanish. Results, considering different configurations of ASR outputs, show the better behavior of the system when a combination of hypotheses is considered.This work is partially supported by the Spanish MICINN under contract TIN2011-28169-C05-01, and under FPU Grant AP2010-4193Calvo Lance, M.; García Granada, F.; Hurtado Oliver, LF.; Jiménez Serrano, S.; Sanchís Arnal, E. (2013). Exploiting multiple ASR outputs for a spoken language understanding task. En Speech and Computer. Springer Verlag (Germany). 8113:138-145. https://doi.org/10.1007/978-3-319-01931-4_19S1381458113Tür, G., Mori, R.D.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, 1st edn. Wiley (2011)Fiscus, J.G.: A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER). In: Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354. IEEE (1997)Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23, 2947–2948 (2007)Sim, K.C., Byrne, W.J., Gales, M.J.F., Sahbi, H., Woodland, P.C.: Consensus network decoding for statistical machine translation system combination. In: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (2007)Bangalore, S., Bordel, G., Riccardi, G.: Computing Consensus Translation from Multiple Machine Translation Systems. In: Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2001), pp. 351–354 (2001)Calvo, M., Hurtado, L.-F., García, F., Sanchís, E.: A Multilingual SLU System Based on Semantic Decoding of Graphs of Words. In: Torre Toledano, D., Ortega Giménez, A., Teixeira, A., González Rodríguez, J., Hernández Gómez, L., San Segundo Hernández, R., Ramos Castro, D. (eds.) IberSPEECH 2012. CCIS, vol. 328, pp. 158–167. Springer, Heidelberg (2012)Hakkani-Tür, D., Béchet, F., Riccardi, G., Tür, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20, 495–514 (2006)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López de Letona, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proceedings of LREC 2006, Genoa, Italy, pp. 1636–1639 (2006

A MACHINE LEARNING FRAMEWORK FOR AUTOMATIC SPEECH RECOGNITION IN AIR TRAFFIC CONTROL USING WORD LEVEL BINARY CLASSIFICATION AND TRANSCRIPTION

Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to solve these problems by presenting a machine learning framework which uses word-by-word audio samples to transcribe ATC speech. Instead of transcribing an entire speech sample, this framework transcribes every word individually. Then, overall transcription is pieced together based on the word sequence. Each stage of the framework is trained and tested independently of one another, and the overall performance is gauged. The overall framework was gauged to be a feasible approach to ASR in ATC

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system