12,417 research outputs found
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
LipLearner: Customizable Silent Speech Interactions on Mobile Devices
Silent speech interface is a promising technology that enables private
communications in natural language. However, previous approaches only support a
small and inflexible vocabulary, which leads to limited expressiveness. We
leverage contrastive learning to learn efficient lipreading representations,
enabling few-shot command customization with minimal user effort. Our model
exhibits high robustness to different lighting, posture, and gesture conditions
on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947
is achievable only using one shot, and its performance can be further boosted
by adaptively learning from more data. This generalizability allowed us to
develop a mobile silent speech interface empowered with on-device fine-tuning
and visual keyword spotting. A user study demonstrated that with LipLearner,
users could define their own commands with high reliability guaranteed by an
online incremental learning scheme. Subjective feedback indicated that our
system provides essential functionalities for customizable silent speech
interactions with high usability and learnability.Comment: Conditionally accepted to the ACM CHI Conference on Human Factors in
Computing Systems 2023 (CHI '23
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information
Audio-visual speech recognition (AVSR) gains increasing attention from
researchers as an important part of human-computer interaction. However, the
existing available Mandarin audio-visual datasets are limited and lack the
depth information. To address this issue, this work establishes the MAVD, a new
large-scale Mandarin multimodal corpus comprising 12,484 utterances spoken by
64 native Chinese speakers. To ensure the dataset covers diverse real-world
scenarios, a pipeline for cleaning and filtering the raw text material has been
developed to create a well-balanced reading material. In particular, the latest
data acquisition device of Microsoft, Azure Kinect is used to capture depth
information in addition to the traditional audio signals and RGB images during
data acquisition. We also provide a baseline experiment, which could be used to
evaluate the effectiveness of the dataset. The dataset and code will be
released at https://github.com/SpringHuo/MAVD
Audio-visual speech processing system for Polish applicable to human-computer interaction
This paper describes audio-visual speech recognition system for Polish language and a set of performance tests under various acoustic conditions. We first present the overall structure of AVASR systems with three main areas: audio features extraction, visual features extraction and subsequently, audiovisual speech integration. We present MFCC features for audio stream with standard HMM modeling technique, then we describe appearance and shape based visual features. Subsequently we present two feature integration techniques, feature concatenation and model fusion. We also discuss the results of a set of experiments conducted to select best system setup for Polish, under noisy audio conditions. Experiments are simulating human-computer interaction in computer control case with voice commands in difficult audio environments. With Active Appearance Model (AAM) and multistream Hidden Markov Model (HMM) we can improve system accuracy by reducing Word Error Rate for more than 30%, comparing to audio-only speech recognition, when Signal-to-Noise Ratio goes down to 0dB
Vowel priority lip matching scheme and similarity evaluation model based on humanoid robot Ren-Xin
At present, the significance of humanoid robots dramatically increased while this kind of robots rarely enters human life because of its immature development. The lip shape of humanoid robots is crucial in the speech process since it makes humanoid robots look like real humans. Many studies show that vowels are the essential elements of pronunciation in all languages in the world. Based on the traditional research of viseme, we increased the priority of the smooth transition of lip between vowels and propose a lip matching scheme based on vowel priority. Additionally, we also designed a similarity evaluation model based on the Manhattan distance by using computer vision lip features, which quantifies the lip shape similarity between 0-1 provides an effective recommendation of evaluation standard. Surprisingly, this model successfully compensates the disadvantages of lip shape similarity evaluation criteria in this field. We applied this lip-matching scheme to Ren-Xin humanoid robot and performed robot teaching experiments as well as a similarity comparison experiment of 20 sentences with two males and two females and the robot. Notably, all the experiments have achieved excellent results
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