Summary:
Poddar et al. use HMMs and speech to recognize complementary keyword-gesture pairs in weather reporting. The multi-modal system would combine speech and gesture recognition to improve recognition accuracy.
HMMs work well for separating 20 isolated gestures from weather narrators, but continuous gestures drop the accuracy to between 50-80% for sequences. Co-occurrence analysis seeks to study the meaning behind keywords occurring with gestures, the frequency behind these occurrences, and the temporal alignment of the gestures and keywords. A table they presented shows that certain types of gestures (contour, area, and pointing) are more heavily associated with certain keywords ("here", "direction", "location"). The accuracy of recognizing the gestures can improve with both video (gesture) and audio (keywords).
Discussion:
This paper does not necessarily add much to solutions, but it was written ten years ago and did show some nice results that combining speech and gesture can improve recognition. Since the authors did not use a speech recognition system, the errors with that system would also produce interesting results that differ from the given accuracies.
Showing posts with label speech. Show all posts
Showing posts with label speech. Show all posts
Wednesday, April 23, 2008
Monday, April 14, 2008
Glove-TalkII--A Neural-Network Interface which Maps Gestures to Parallel Formant Speech Synthesizer Controls
Summary:
Fels and Hinton created Glove-TalkII, a system designed to synthesize voice using complicated glove and feet controls.
The artificial vocal track (AVT) is controlled using a CyberGlove, ContactGlove, polhemus sensor, and foot pedal. The ContactGlove controls 9 stop consonants, such as CH, T, and NG. The foot pedal controls the volume of the speech. Hand position corresponds to a vowel sound. Hand postures map to nonstop consonant phonemes.
The neural networks used include a vowel/consonant network to determine if the sensors are reading a vowel or consonant, and then separate vowel and consonant networks to distinguish between the phonemes.
A single user had to undergo 100 hours of training to be able to use the system.
Discussion:
Impractical. I'm shocked that they had someone train the system for 100 hours, and the fact that it takes a person that long to train the system should indicate that this is a poor way to synthesize voice. The person's final voice is even described as "intelligible and somewhat natural-sounding", which is not a good complement.
Requiring a person to walk around with a one-handed keyboard and type their words is a better solution. The keyboard wouldn't even have a foot pedal.
Fels and Hinton created Glove-TalkII, a system designed to synthesize voice using complicated glove and feet controls.
The artificial vocal track (AVT) is controlled using a CyberGlove, ContactGlove, polhemus sensor, and foot pedal. The ContactGlove controls 9 stop consonants, such as CH, T, and NG. The foot pedal controls the volume of the speech. Hand position corresponds to a vowel sound. Hand postures map to nonstop consonant phonemes.
The neural networks used include a vowel/consonant network to determine if the sensors are reading a vowel or consonant, and then separate vowel and consonant networks to distinguish between the phonemes.
A single user had to undergo 100 hours of training to be able to use the system.
Discussion:
Impractical. I'm shocked that they had someone train the system for 100 hours, and the fact that it takes a person that long to train the system should indicate that this is a poor way to synthesize voice. The person's final voice is even described as "intelligible and somewhat natural-sounding", which is not a good complement.
Requiring a person to walk around with a one-handed keyboard and type their words is a better solution. The keyboard wouldn't even have a foot pedal.
Wednesday, November 7, 2007
Speech and Sketching: An Empirical Study of Multimodal Interaction
Summary:
In this paper, Adler and Davis explore multimodal speech and sketch interfaces through a user study. Their goal is to allow the computer to provide feedback to the user as the user talks and draws, and the computer will influence the design during this process by asking questions and clarifying information. Having the computer understand everything about the design is not the goal; instead, the computer should know enough to ask motivating questions when necessary in order to engage the user. The system also does not want to constrain the user's drawing or speech style.
The user study conducted involved 18 users in a Wizard-of-Oz study. The users were asked to design a floor plan, full adder, AC/DC transformer, and a digital circuit. Sketches was done on Tablet PCs in software that allowed for drawing and highlighting in 5 different colors. During the study, the experimenter sat at a table across from the user. The study was filmed and the audio, visual, and sketching components of the study were synchronized.
The study showed some interesting results concerning color, questions, and speech timing. Users tended to rely on multiple colors to indicate portions of the sketch. The color linked parts of the sketch together, referred back to previous parts, and reflected the real-world colors of objects. When speaking, users typically had phrase and word repetition when they were thinking aloud. This could allow the computer to discern key words from the user-computer dialogue. Responses from computer questions also caused the user to repeat the questions, and simple questions could prompt more information than what was asked. Some users even redesigned their drawings after simple questions were asked, such as inquiring if two objects were similar. Speech and sketching started simultaneously in the study. Yet, certain parts of the speech, such as an entire phrase, tended to start before the sketch, and certain key words said alone tended to be heard after a sketch was started.
Discussion:
The two best components of Adler's study show how computers can assist humans during design steps by relying on the human design and thought process, instead of having an actual understanding. In lieu of training the computer to understand all of the components of a design, basic understanding of object similarity and grouping should be enough to produce a motivating dialogue. Also, the fact that the user constantly repeats words provides the computer with an indication of important information without the need of a large vocabulary.
I wish the study also went into more interface issues, such as when the computer should ask a question (e.g. during sketching, during a pause, etc.). Also, it would have been beneficial to see the average pause time of a user and if the user was speaking or mumbling during the pause by going "hmm" or something similar. Do the pauses for sketch indicate that the user is speaking, and do pauses for speaking indicate the user is sketching? Do the pauses for both modes line up?
In this paper, Adler and Davis explore multimodal speech and sketch interfaces through a user study. Their goal is to allow the computer to provide feedback to the user as the user talks and draws, and the computer will influence the design during this process by asking questions and clarifying information. Having the computer understand everything about the design is not the goal; instead, the computer should know enough to ask motivating questions when necessary in order to engage the user. The system also does not want to constrain the user's drawing or speech style.
The user study conducted involved 18 users in a Wizard-of-Oz study. The users were asked to design a floor plan, full adder, AC/DC transformer, and a digital circuit. Sketches was done on Tablet PCs in software that allowed for drawing and highlighting in 5 different colors. During the study, the experimenter sat at a table across from the user. The study was filmed and the audio, visual, and sketching components of the study were synchronized.
The study showed some interesting results concerning color, questions, and speech timing. Users tended to rely on multiple colors to indicate portions of the sketch. The color linked parts of the sketch together, referred back to previous parts, and reflected the real-world colors of objects. When speaking, users typically had phrase and word repetition when they were thinking aloud. This could allow the computer to discern key words from the user-computer dialogue. Responses from computer questions also caused the user to repeat the questions, and simple questions could prompt more information than what was asked. Some users even redesigned their drawings after simple questions were asked, such as inquiring if two objects were similar. Speech and sketching started simultaneously in the study. Yet, certain parts of the speech, such as an entire phrase, tended to start before the sketch, and certain key words said alone tended to be heard after a sketch was started.
Discussion:
The two best components of Adler's study show how computers can assist humans during design steps by relying on the human design and thought process, instead of having an actual understanding. In lieu of training the computer to understand all of the components of a design, basic understanding of object similarity and grouping should be enough to produce a motivating dialogue. Also, the fact that the user constantly repeats words provides the computer with an indication of important information without the need of a large vocabulary.
I wish the study also went into more interface issues, such as when the computer should ask a question (e.g. during sketching, during a pause, etc.). Also, it would have been beneficial to see the average pause time of a user and if the user was speaking or mumbling during the pause by going "hmm" or something similar. Do the pauses for sketch indicate that the user is speaking, and do pauses for speaking indicate the user is sketching? Do the pauses for both modes line up?
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