Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases — such as the speaker’s contacts or recent song playlists — during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus (“ConEC”) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants’ names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology

Michael Johnston, John Chen, Patrick Ehlen, Hyuckchul Jung, Jay Lieske, Aarthi Reddy, Ethan Selfridge, Svetlana Stoyanchev, Brant Vasilieff, Jay Wilpon. Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). 2014.

Meeting participants can experience cognitive overload when they need both to verbally contribute to ongoing discussion while simultaneously creating notes to promote later recall of decisions made during the meeting. We designed two novel cuing tools to reduce the cognitive load associated with note-taking, thus improving verbal contributions in meetings. The tools combine real-time automatic speech recognition (ASR) with lightweight annotation to transform note-taking into a low overhead markup process. To create lightweight notes, users do not generate the notes' content themselves. Instead they simply highlight important phrases in a real-time ASR transcript (Highlighter tool), or press a button to indicate when they heard something important (Hotspots tool). We evaluated these markup tools against a traditional pen-and-paper baseline with 26 users. Hotspots was highly successful: compared with handwritten notes, it increased participants' conversational contributions and reduced their perception of overload in the meeting, while improving recall of the meeting two months later. Highlighter also improved recall without compromising conversational contributions, although users found it more demanding.

Speak4itSM is a multimodal, mobile search application that provides information about local businesses. Users can combine speech and touch input simultaneously to make search queries or commands to the application. For example, a user might say, "gas stations", while simultaneously tracing a route on a touchscreen. In this demonstration, we describe the extension of our multimodal semantic processing architecture and application from a one-shot query system to a multimodal dialogue system that tracks dialogue state over multiple turns. We illustrate the capabilities and limitations of an information-state-based approach to multimodal interpretation. We provide interactive demonstrations of Speak4it on a tablet and a smartphone, and explain the challenges of supporting true multimodal interaction in a deployed mobile service.

Speak4it™ is a mobile search application that leverages multimodal input and integration to allow users to search for and act on local business information. We present an initial empirical analysis of user interaction with a multimodal local search application deployed in the field with real users. Specifically, we focus on queries involving multimodal commands, and analyze multimodal interaction behaviors seen in a deployed multimodal system.

Multimodal interaction allows users to specify commands using combinations of inputs from multiple different modalities. For example, in a local search application, a user might say "gas stations" while simultaneously tracing a route on a touchscreen display. In this demonstration, we describe the extension of our cloud-based speech recognition architecture to a Multimodal Semantic Interpretation System (MSIS) that supports processing of multimodal inputs streamed over HTTP. We illustrate the capabilities of the framework using Speak4itSM, a deployed mobile local search application supporting combined speech and gesture input. We provide interactive demonstrations of Speak4it on the iPhone and iPad and explain the challenges of supporting true multimodal interaction in a deployed mobile service.

Speak4it is a consumer-oriented mobile search application that leverages multimodal input and output to allow users to search for and act on local business information. It supports true multimodal integration where user inputs can be distributed over multiple input modes. In addition to specifying queries by voice e.g. bike repair shops near the golden gate bridge users can combine speech and gesture, for example, gas stations + will return the gas stations along the specified route traced on the display. We describe the underlying multimodal architecture and some challenges of supporting multimodal interaction as a deployed mobile service.

Speak4itSMis a consumer-oriented mobile search application that leverages multimodal input and output to allow users to search for and act on local business information. In addition to specifying queries by voice (e.g., "bike repair shops near the Golden Gate Bridge") users can combine speech and gesture. For example, "gas stations" +; will return the gas stations along the specified route traced on the display. We provide interactive demonstrations of Speak4it on both the iPhone and iPad platforms and explain the underlying multimodal architecture and challenges of supporting true multimodal interaction in a deployed mobile service.

We address the problem of identifying words and phrases that accurately capture, or contribute to, the semantic gist of decisions made in multi-party human-human meetings. We first describe our approach to modelling decision discussions in spoken meetings and then compare two approaches to extracting information from these discussions. The first one uses an opendomain semantic parser that identifies candidate phrases for decision summaries and then employs machine learning techniques to select from those candidate phrases. The second one uses categorical and sequential classifiers that exploit simple syntactic and semantic features to identify words and phrases relevant for decision summarization.

Upcoming technologies will automatically identify and extract certain types of general information from meetings, such as topics and the tasks people agree to do. We explore interfaces for presenting this information to users after a meeting is completed, using two post-meeting interfaces that display information from topics and action items respectively. These interfaces also provide an excellent forum for obtaining user feedback about the performance of classification algorithms, allowing the system to learn and improve with time. We describe how we manage the delicate balance of obtaining necessary feedback without overburdening users. We also evaluate the effectiveness of feedback from one interface on improvement of future action item detection.

Mobile interfaces need to allow the user and system to adapt their choice of communication modes according to user preferences, the task at hand, and the physical and social environment. We describe a multimodal application architecture which combines finite-state multimodal language processing, a speech-act based multimodal dialogue manager, dynamic multimodal output generation, and user-tailored text planning to enable rapid prototyping of multimodal interfaces with flexible input and adaptive output. Our testbed application MATCH (Multimodal Access To City Help) provides a mobile multimodal speech-pen interface to restaurant and sub-way information for New York City.

Multimodal interfaces offer users unprecedented flexibility in choosing a style of interaction. However, users are frequently unaware of or forget shorter or more effective multimodal or pen-based commands. This paper describes a working help system that leverages the capabilities of a multimodal interface in order to provide targeted, unobtrusive, context-sensitive help. This multimodal help system guides the user to the most effective way to specify a request, providing transferable knowledge that can be used in future requests without repeatedly invoking the help system.

Next-generation multimodal systems designed for use in mobile environments present challenges to the task of data collection that are not faced by speech-based systems. We discuss some established data collection and evaluation methods and their limitations in the context of a mobile multimodal system. These limitations are addressed by the "on-board" multimodal data collection method developed for MATCH, a multimodal mobile city guide. Our approach exploits MATCH's component architecture in that each component can be redeployed in evaluation and annotation tools, allowing user test sessions to be replayed with a high degree of fidelity without the use of recorded video. Instead, the components themselves perform a dynamic re-enactment of test sessions directed by the script of a comprehensive log file. This method enabled continual user testing and piloting to inform the iterative development process for MATCH.

Interfaces for mobile information access need to allow users flexibility in their choice of modes and interaction style in accordance with their preferences, the task at hand, and their physical and social environment. This paper describes the approach to multimodal language processing in MATCH (Multimodal Access To City Help), a mobile multimodal speech-pen interface to restaurant and subway information for New York City. Finite-state methods for multimodal integration and understanding enable users to interact using pen, speech, or dynamic combinations of the two, and a speech-act based multimodal dialogue manager enables mixedinitiative multimodal dialogue. 1. LANGUAGE PROCESSING FOR MOBILE SYSTEMS Mobile information access devices (PDAs, tablet PCs, next generation phones) offer limited screen real estate and no keyboard or