And yet more on context switching for the voice assistant. Here work by Amazon, to be presented at an upcoming conference. Originating from work to ignore 'wake words' in 'second screens' from other media. Which seems to work quite well now. But immediately made me think of: Why not include more data in the watermark to identify it further, transmit information it learns about a context. Nice direction.
Audio Watermarking Algorithm Is First to Solve "Second-Screen Problem" in Real Time By Yuan-yen Tai
Audio watermarking is the process of adding a distinctive sound pattern — undetectable to the human ear — to an audio signal to make it identifiable to a computer. It’s one of the ways that video sites recognize copyrighted recordings that have been posted illegally.
To identify a watermark, a computer usually converts a digital file into an audio signal, which it processes internally. If the watermark were embedded in the digital file, rather than in the signal itself, then re-encoding the audio in a different file format would eliminate the watermark.
Watermarking schemes designed for on-device processing tend to break down, however, when a signal is broadcast over a loudspeaker, captured by a microphone, and only then inspected for watermarks. In what is referred to as the second-screen problem, noise and interference distort the watermark, and delays from acoustic transmission make it difficult to synchronize the detector with the signal.
At this year’s International Conference on Acoustics, Speech, and Signal Processing, in May, Amazon senior research scientist Mohamed Mansour and I will present a new audio-watermarking algorithm that effectively solves the second-screen problem in real time for the first time in the watermarking literature. .... "
Showing posts with label Wake Words. Show all posts
Showing posts with label Wake Words. Show all posts
Monday, April 22, 2019
Wake Words for Assistance Context
Been experiencing the strange concept of a 'wake word' for a few years now. Its means of switching context ... saying that after I say this special word or phrase, you can interpret everything I said afterwards as special, like a command. My Echos and Google Homes and Siri do that. Sometimes well, some times not. And if there are multiple devices, what if several 'wake'?
It seems that some devices, in certain places, can do it better or worse, leading to misinterpretation. Sometimes this is annoying, even dangerous. I have set timers, and when I didn't carefully wait for a confirmation, discovered they were not set. Its about the acoustics and expectations, I understand, like when you are talking to people. This technical article shows there is lots going on with the wake word now:
Using Wake Word Acoustics to Filter Out Background Speech Improves Speech Recognition by 15% By Xing Fan Amazon Alexa.
One of the ways that we’re always trying to improve Alexa’s performance is by teaching her to ignore speech that isn’t intended for her.
At this year’s International Conference on Acoustics, Speech, and Signal Processing, my colleagues and I will present a new technique for doing this, which could complement the techniques that Alexa already uses.
We assume that the speaker who activates an Alexa-enabled device by uttering its “wake word” — usually “Alexa” — is the one Alexa should be listening to. Essentially, our technique takes an acoustic snapshot of the wake word and compares subsequent speech to it. Speech whose acoustics match those of the wake word is judged to be intended for Alexa, and all other speech is treated as background noise.
Rather than training a separate neural network to make this discrimination, we integrate our wake-word-matching mechanism into a standard automatic-speech-recognition system. We then train the system as a whole to recognize only the speech of the wake word utterer. In tests, this approach reduced speech recognition errors by 15%.
We implemented our technique using two different neural-network architectures. Both were variations of a sequence-to-sequence encoder-decoder network with an attention mechanism. A sequence-to-sequence network is one that processes an input sequence — here, a series of “frames”, or millisecond-scale snapshots of an audio signal — in order and produces a corresponding output sequence — here, phonetic renderings of speech sounds. ... "
It seems that some devices, in certain places, can do it better or worse, leading to misinterpretation. Sometimes this is annoying, even dangerous. I have set timers, and when I didn't carefully wait for a confirmation, discovered they were not set. Its about the acoustics and expectations, I understand, like when you are talking to people. This technical article shows there is lots going on with the wake word now:
Using Wake Word Acoustics to Filter Out Background Speech Improves Speech Recognition by 15% By Xing Fan Amazon Alexa.
One of the ways that we’re always trying to improve Alexa’s performance is by teaching her to ignore speech that isn’t intended for her.
At this year’s International Conference on Acoustics, Speech, and Signal Processing, my colleagues and I will present a new technique for doing this, which could complement the techniques that Alexa already uses.
We assume that the speaker who activates an Alexa-enabled device by uttering its “wake word” — usually “Alexa” — is the one Alexa should be listening to. Essentially, our technique takes an acoustic snapshot of the wake word and compares subsequent speech to it. Speech whose acoustics match those of the wake word is judged to be intended for Alexa, and all other speech is treated as background noise.
Rather than training a separate neural network to make this discrimination, we integrate our wake-word-matching mechanism into a standard automatic-speech-recognition system. We then train the system as a whole to recognize only the speech of the wake word utterer. In tests, this approach reduced speech recognition errors by 15%.
We implemented our technique using two different neural-network architectures. Both were variations of a sequence-to-sequence encoder-decoder network with an attention mechanism. A sequence-to-sequence network is one that processes an input sequence — here, a series of “frames”, or millisecond-scale snapshots of an audio signal — in order and produces a corresponding output sequence — here, phonetic renderings of speech sounds. ... "
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