"CU-move" : analysis & corpus development for interactive in-vehicle speech systems

By John H. L. Hansen, Pongtep Angkititrakul, Jay P. Plucienkowski, Stephen Gallant, Umit Yapanel, B. Pellom, Wayne H. Ward, R. Cole. 2001

46 citations recorded by the source corpus.

In this paper, we present our recent work in the analysis and formulation of a new acoustic speech corpus for developing in-vehicle interactive systems for route planning and navigation. The CU-Move Corpus development is partitioned into two phases: [I] acoustic noise collection and analysis across vehicles, and [II] data collection consisting of +1000 speakers from across the United States. We present results from Phase I acoustic noise data analysis across vehicles to determine guidelines for Phase II large-scale data collection using a single vehicle type. A total of 14 noise conditions are identified for analysis across 6 vehicles. We also present our plan for Phase II collection including speakers, dialect regions, data collection hardware, prompts and dialog domains. Since previous studies in speech recognition have shown significant loses in performance when speakers are under task stress, it is important to develop conversational systems that minimize operator stress for the driver. This will be the first U.S. based corpus of its kind consisting of multichannel data, intended for use in developing mixed-initiative dialog speech systems; the initial application being route planning and navigation through a wireless information retrieval sub-system connected to the WWW.

Subfields: Artificial Intelligence, Dialogue & Conversational AI, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kazuya Takeda
2. John H. L. Hansen
3. John H. L. Hansen
4. Chiyomi Miyajima
5. Junichi Yamagishi
6. Tomoki Toda

In Speech & Audio

1. John H. L. Hansen
2. Kazuya Takeda
3. Junichi Yamagishi

Citations in the corpus

• High performance digit recognition in real car environments (2002, background): “The database and noise conditions are analyzed in [2].”
• An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, unknown): “Research in this field has made great progress thanks to real speech corpora collected for various application scenarios such as voice command for cars (Hansen et al., 2001), smart homes (Ravanelli et al.”
• VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “A number of real corpora are now publicly available for environments and application scenarios such as voice command for cars (Aurora-3, 2000; Hansen et al., 2001; Lee et al., 2004) and in public spaces (Barker et al., 2015), automatic transcription of lectures (Lamel et al., 1994), meetings (Janin…”
• International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road (2011, background): “The CU-Move research team at the University of Colorado 1 [ 2 ]‐[4] has collected natural conversational speech from 500 speakers within six cities across the U.S.”