An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.

https://www.sp.nitech.ac.jp/~bonanza/Paper/EMIME/zen_specom.pdf

Statistical Parametric Speech Synthesis

Robust automatic speech recognition with missing and unreliable acoustic data

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.

/pdf/an-overview-of-noise-robust-automatic-speech-recognition-u8zbz18oa3.pdf

An overview of noise-robust automatic speech recognition

The aims of the SpeechDat-Car project are to develop a set of speech databases to support training and testing of multilingual speech recognition applications in the car environment. As a result, a total of ten (10) equivalent and similar resources will be created. The 10 languages are Danish, British English, Finnish, Flemish/Dutch, French, German, Greek, Italian, Spanish and American English. For each language 600 sessions will be recorded (from at least 300 speakers) in seven characteristic environments (low speed, high speed with audio equipment on, etc.). This paper gives an overview of the project with a focus on the production phases (recording platforms, speaker recruitment, annotation and distribution).

/pdf/speechdat-car-a-large-speech-database-for-automotive-301fuo31os.pdf

SPEECHDAT-CAR: a Large Speech Database for Automotive Environments

EUROM-A Spoken Language Resource for the EU

Eurospeech 2001, Scandinavia: 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg Congress and Culture Centre, Aalborg, Denmark

Frame-based speech processing inherently assumes a stationary behavior of speech signals in a short period of time. Over a long time, the characteristics of the signals can change significantly and frames are not equally important, underscoring the need for frame selection. In this paper, we present a low-complexity and effective frame selection approach based on a posteriori signal-to-noise ratio (SNR) weighted energy distance: The use of an energy distance, instead of, e.g., a standard cepstral distance, makes the approach computationally efficient and enables fine granularity search, and the use of a posteriori SNR weighting emphasizes the reliable regions in noisy speech signals. It is experimentally found that the approach is able to assign a higher frame rate to fast changing events such as consonants, a lower frame rate to steady regions like vowels and no frames to silence, even for very low SNR signals. The resulting variable frame rate analysis method is applied to three speech processing tasks that are essential to natural interaction with intelligent environments. First, it is used for improving speech recognition performance in noisy environments. Second, the method is used for scalable source coding schemes in distributed speech recognition where the target bit rate is met by adjusting the frame rate. Third, it is applied to voice activity detection. Very encouraging results are obtained for all three speech processing tasks.

Low-Complexity Variable Frame Rate Analysis for Speech Recognition and Voice Activity Detection

Network Speech Recognition.- Network, Distributed and Embedded Speech Recognition: An Overview.- Speech Coding and Packet Loss Effects on Speech and Speaker Recognition.- Speech Recognition Over Mobile Networks.- Speech Recognition Over IP Networks.- Distributed Speech Recognition.- Distributed Speech Recognition Standards.- Speech Feature Extraction and Reconstruction.- Quantization of Speech Features: Source Coding.- Error Recovery: Channel Coding and Packetization.- Error Concealment.- Embedded Speech Recognition.- Algorithm Optimizations: Low Computational Complexity.- Algorithm Optimizations: Low Memory Footprint.- Fixed-Point Arithmetic.- Systems and Applications.- Software Architectures for Networked Mobile Speech Applications.- Speech Recognition in Mobile Phones.- Handheld Speech to Speech Translation System.- Automotive Speech Recognition.- Energy Aware Speech Recognition for Mobile Devices.

Børge Lindberg

Papers

SPEECHDAT-CAR: a Large Speech Database for Automotive Environments

EUROM-A Spoken Language Resource for the EU

Eurospeech 2001, Scandinavia: 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg Congress and Culture Centre, Aalborg, Denmark

Low-Complexity Variable Frame Rate Analysis for Speech Recognition and Voice Activity Detection

Automatic speech recognition on mobile devices and over communication networks