Computer speech synthesis: its status and prospects.

Computer speech synthesis has reached a high level of performance, with increasingly sophisticated models of linguistic structure, low error rates in text analysis, and high intelligibility in synthesis from phonemic input. Mass market applications are beginning to appear. However, the results are still not good enough for the ubiquitous application that such technology will eventually have. A number of alternative directions of current research aim at the ultimate goal of fully natural synthetic speech. One especially promising trend is the systematic optimization of large synthesis systems with respect to formal criteria of evaluation. Speech recognition has progressed rapidly in the past decade through such approaches, and it seems likely that their application in synthesis will produce similar improvements.

Integration of speech with natural language understanding.

The integration of speech recognition with natural language understanding raises issues of how to adapt natural language processing to the characteristics of spoken language; how to cope with errorful recognition output, including the use of natural language information to reduce recognition errors; and how to use information from the speech signal, beyond just the sequence of words, as an aid to understanding. This paper reviews current research addressing these questions in the Spoken Language Program sponsored by the Advanced Research Projects Agency (ARPA). I begin by reviewing some of the ways that spontaneous spoken language differs from standard written language and discuss methods of coping with the difficulties of spontaneous speech. I then look at how systems cope with errors in speech recognition and at attempts to use natural language information to reduce recognition errors. Finally, I discuss how prosodic information in the speech signal might be used to improve understanding.

Commercial applications of speech interface technology: an industry at the threshold.

Speech interface technology, which includes automatic speech recognition, synthetic speech, and natural language processing, is beginning to have a significant impact on business and personal computer use. Today, powerful and inexpensive microprocessors and improved algorithms are driving commercial applications in computer command, consumer, data entry, speech-to-text, telephone, and voice verification. Robust speaker-independent recognition systems for command and navigation in personal computers are now available; telephone-based transaction and database inquiry systems using both speech synthesis and recognition are coming into use. Large-vocabulary speech interface systems for document creation and read-aloud proofing are expanding beyond niche markets. Today's applications represent a small preview of a rich future for speech interface technology that will eventually replace keyboards with microphones and loud-speakers to give easy accessibility to increasingly intelligent machines.

Toward the ultimate synthesis/recognition system.

This paper predicts speech synthesis, speech recognition, and speaker recognition technology for the year 2001, and it describes the most important research problems to be solved in order to arrive at these ultimate synthesis and recognition systems. The problems for speech synthesis include natural and intelligible voice production, prosody control based on meaning, capability of controlling synthesized voice quality and choosing individual speaking style, multilingual and multidialectal synthesis, choice of application-oriented speaking styles, capability of adding emotion, and synthesis from concepts. The problems for speech recognition include robust recognition against speech variations, adaptation/normalization to variations due to environmental conditions and speakers, automatic knowledge acquisition for acoustic and linguistic modeling, spontaneous speech recognition, naturalness and ease of human-machine interaction, and recognition of emotion. The problems for speaker recognition are similar to those for speech recognition. The research topics related to all these techniques include the use of articulatory and perceptual constraints and evaluation methods for measuring the quality of technology and systems.

Speech technology in 2001: new research directions.

Research in speech recognition and synthesis over the past several decades has brought speech technology to a point where it is being used in "real-world" applications. However, despite the progress, the perception remains that the current technology is not flexible enough to allow easy voice communication with machines. The focus of speech research is now on producing systems that are accurate and robust but that do not impose unnecessary constraints on the user. This chapter takes a critical look at the shortcomings of the current speech recognition and synthesis algorithms, discusses the technical challenges facing research, and examines the new directions that research in speech recognition and synthesis must take in order to form the basis of new solutions suitable for supporting a wide range of applications.

Reconhecimento de monossílabos em idosos: análise do nível de apresentação da fala

A perda auditiva no idoso acarreta em dificuldade na percepção da fala. O teste comumente utilizado na logoaudiometria é a pesquisa do índice de reconhecimento de fala máximo (IR-Max) em uma única intensidade de apresentação da fala. Entretanto, o procedimento mais adequado seria a realização do teste em diversas intensidades, visto que o índice de acerto depende da intensidade da fala no momento do teste e está relacionado com o grau e configuração da perda auditiva. A imprecisão na obtenção do IR-Max poderá gerar uma hipótese diagnóstica errônea e o insucesso no processo de intervenção na perda auditiva. Objetivo: Verificar a interferência do nível de apresentação da fala, no teste de reconhecimento de fala, em idosos com perda auditiva sensorioneural com diferentes configurações audiométricas. Métodos: Participaram 64 idosos, 120 orelhas (61 do gênero feminino e 59 do gênero masculino), idade entre 60 e 88 anos, divididos em grupos: G1- composto por 23 orelhas com configuração horizontal, G2- 55 orelhas com configuração descendente, G3- 42 orelhas com configuração abrupta. Os critérios de inclusão foram: perda auditiva sensorioneural de grau leve a severo, não usuário de aparelho de amplificação sonora individual (AASI), ou com tempo de uso inferior a dois meses, e ausência de alterações cognitivas. Foram realizados os seguintes procedimentos: pesquisas do limiar de reconhecimento de fala (LRF), do índice de reconhecimento de fala (IRF) em diversas intensidades e do nível de máximo conforto (MCL) e desconforto (UCL) para a fala. Para tal, foram utilizadas listas com 11 monossílabos, para diminuir a duração do teste. A análise estatística foi composta pelo teste Análise de Variância (ANOVA) e teste de Tukey. Resultados: A configuração descendente foi a de maior ocorrência. Indivíduos com configuração horizontal apresentaram índice médio de acerto mais elevado de reconhecimento de fala. Ao considerar o total avaliado, 27,27% dos indivíduos com configuração horizontal revelaram o IR-Max no MCL, assim como 38,18% com configuração descendente e 26,19% com configuração abrupta. O IR-Max foi encontrado no UCL, em 40,90% dos indivíduos com configuração horizontal, 45,45% com configuração descendente e 28,20% com configuração abrupta. Respectivamente, o maior e o menor índice médio de acerto foram encontrados em: G1- 30 e 40 dBNS; G2- 50 e 10 dBNS; G3- 45 e 10 dBNS. Não há uma única intensidade de fala a ser utilizada em todos os tipos de configurações audiométricas, entretanto, os níveis de sensação que identificaram os maiores índices médios de acerto foram: G1- 20 a 30 dBNS, G2- 20 a 50 dBNS; G3- 45 dBNS. O MCL e o UCL-5 dB para a fala não foram eficazes para determinar o IR-Max. Conclusões: O nível de apresentação teve influência no desempenho no reconhecimento de fala para monossílabos em idosos com perda auditiva sensorioneural em todas as configurações audiométricas. A perda auditiva de grau moderado e a configuração audiométrica descendente foram mais frequentes nessa população, seguida da abrupta e horizontal.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

Google Translate e Microsoft Translator - Valutazione di due applicazioni per la traduzione automatica del parlato e analisi di una tecnologia in evoluzione.

This work focuses on Machine Translation (MT) and Speech-to-Speech Translation, two emerging technologies that allow users to automatically translate written and spoken texts. The first part of this work provides a theoretical framework for the evaluation of Google Translate and Microsoft Translator, which is at the core of this study. Chapter one focuses on Machine Translation, providing a definition of this technology and glimpses of its history. In this chapter we will also learn how MT works, who uses it, for what purpose, what its pros and cons are, and how machine translation quality can be defined and assessed. Chapter two deals with Speech-to-Speech Translation by focusing on its history, characteristics and operation, potential uses and limits deriving from the intrinsic difficulty of translating spoken language. After describing the future prospects for SST, the final part of this chapter focuses on the quality assessment of Speech-to-Speech Translation applications. The last part of this dissertation describes the evaluation test carried out on Google Translate and Microsoft Translator, two mobile translation apps also providing a Speech-to-Speech Translation service. Chapter three illustrates the objectives, the research questions, the participants, the methodology and the elaboration of the questionnaires used to collect data. The collected data and the results of the evaluation of the automatic speech recognition subsystem and the language translation subsystem are presented in chapter four and finally analysed and compared in chapter five, which provides a general description of the performance of the evaluated apps and possible explanations for each set of results. In the final part of this work suggestions are made for future research and reflections on the usability and usefulness of the evaluated translation apps are provided.

Panoramic view of the city of Ann Arbor, Washtenaw Co. Michigan, 1880

Publication information: Madison, Wis. : J.J. Stoner [1880}

Panoramic view of the city of Ann Arbor, Washtenaw Co. Michigan, 1880

not drawn to scale; Oriented with north to the upper right.

Aerial view of the main campus, University of Michigan, 1930

On verso: This Aerial View released with full REPRODUCTION RIGHTS RESERVED. If reproduced by written permission full credit for production must be given. Talbert Abrams - President, A-S-C AiIRLINE CORPORATION, Lansing, Michigan

Aerial view of campus

Looking northwest from above Washtenaw and South University

Aerial view of Hill Auditorium, Michigan League and Rackham

Taken just south of Hill

Aeroplane view of Ann Arbor 1937

Looking at central campus from the south

Submodular Optimization and Data Processing

Thesis (Ph.D.)--University of Washington, 2016-06