Automotive Speech Recognition

Höge, Harald; Hohenner, Sascha; Kämmerer, Bernhard R.; Kunstmann, Niels; Schachtl, Stefanie; Schonle, M.; Setiawan, Panji

doi:10.1007/978-1-84800-143-5_16

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…As this might be due to the cost and human resource investments associated with proper adoption, we feel that this will initially stay an important obstacle and that its use in the near future will stay a privilege for online retailers selling more expensive products. However, as conversational interface devices (such as Spoken Dialog Systems) and humanlike agents that can "talk" to customers using data-rich semantic protocols will probably become readily accessible in the somewhat further future, this obstacle should be overcome easily, making the prospect of daily interactions with hundreds of millions of people within the system over mobile devices or communication networks actually feasible (Höge et al, 2008;Pieraccini, 2009). While most online retailers currently only feature text-chat help, for the future we see an evolution towards audio-and video-chat help features.…”

Section: Discussionmentioning

confidence: 99%

Adoption of Social Media by Online Retailers

Karimov

Brengman

2011

International Journal of E-Entrepreneurship and Innovation

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In the online environment, the absence of social presence may prevent consumers from purchasing online, while it can enhance their trust, loyalty and enjoyment toward the e-retailer. Thus, today many online retailers try to create social presence by adopting media-rich technologies. In this paper, the authors assess to what degree social media cues are currently adopted by thriving web-vendors and on that basis speculate about future developments. To this purpose, 210 top B2C e-commerce websites have been content analyzed to identify how they differ in the deployment of diverse social media cues. While a wide range of social media cues are adopted by a majority of top e-retailers, a number of more advanced social media features like avatars, recommendation agents, and video-streams are in their infancy where adoption is concerned. The paper demonstrates that the utilization of social media features differs according to the monetary and symbolic value of products sold by the e-commerce vendors.

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Section: Discussionmentioning

confidence: 99%

Adoption of Social Media by Online Retailers

Karimov

Brengman

2011

International Journal of E-Entrepreneurship and Innovation

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“…If speech-based services such as hands-free telephony [1,2], in-car communication [3,4], or voice control [5] should be used in cars, microphones that convert the acoustic signals into electric counterparts are required. In order to capture the speech signals of the passengers in an optimal way, the question on the placement of the microphones is natural.…”

Section: Introductionmentioning

confidence: 99%

Signal processing techniques for seat belt microphone arrays

Rajan

Krini²,

Rodemer³

et al. 2016

EURASIP J. Adv. Signal Process.

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Microphones integrated on a seat belt are an interesting alternative to conventional sensor positions used for handsfree telephony or speech dialog systems in automobile environments. In the setup presented in this contribution, the seat belt consists of three microphones which usually lay around the shoulder and chest of a sitting passenger. The main benefit of belt microphones is the small distance from the talker's mouth to the sensor. As a consequence, an improved signal quality in terms of a better signal-to-noise ratio (SNR) compared to other sensor positions, e.g., at the rear view mirror, the steering wheel, or the center console, can be achieved. However, the belt microphone arrangement varies considerably due to movements of the passenger and depends on the size of the passenger. Furthermore, additional noise sources arise for seat belt microphones: they can easily be touched, e.g., by clothes, or might be in the path of an air-stream from the automotive ventilation system. This contribution presents several robust signal enhancement algorithms designed for belt microphones in multi-seat scenarios. The belt microphone with the highest SNR (usually closest to the speaker's mouth) is selected for speech signal enhancement. Further improvements can be achieved if all belt microphone signals are combined to a single output signal. The proposed signal enhancement system for belt microphones includes a robust echo cancelation scheme, three different microphone combining approaches, a sophisticated noise estimation scheme to track stationary as well as non-stationary noise, and a speech mixer to combine the signals from each seat belt to a single channel output in a multi-seat scenario.

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“…Speech applications have been deployed in vehicles for years [2]. The intent is generally to simplify the user interaction with infotainment systems and to provide an additional method of user interaction while driving.…”

Section: Introductionmentioning

confidence: 99%

Language pattern analysis for automotive natural language speech applications

Winter

Grost²,

Tsimhoni

2010

Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Natural language speech user interfaces offer a compelling choice of user interaction for the automotive market. With the increasing number of domains in which speech applications are applied, drivers must currently memorize many command words to control traditional speech interfaces. In contrast, natural language interfaces demand only a basic understanding of the system model instead of memorizing keywords and predefined patterns. To utilize natural language interfaces optimally, designers need to better comprehend how people utter their requests to express their intentions. In this study, we collected a corpus of utterances from users who interacted freely with an automotive natural language speech application. We analyzed the corpus by employing a corpus linguistic technique. As a result, natural language utterances can be classified into three components: information data, context relevant words, and non context relevant vocabulary. Applying this classification, users tended to repeat similar utterance patterns composed from a very limited set of different words. Most of the vocabulary in longer utterances was found to be non context restrictive providing no information. Moreover, users could be distinguished by their language patterns. Finally, this information can be used for the development of natural language speech applications. Some initial ideas are discussed in the paper.

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Automotive Speech Recognition

Cited by 5 publications

References 8 publications

Adoption of Social Media by Online Retailers

Adoption of Social Media by Online Retailers

Signal processing techniques for seat belt microphone arrays

Language pattern analysis for automotive natural language speech applications

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