Smart IoT Speakers, while connected over a network, currently only produce sounds that come directly from the individual devices. We envision a future where smart speakers collaboratively produce a fabric of spatial audio, capable of perceptually placing sound in a range of locations in physical space. This could provide audio cues in homes, offices and public spaces that are flexibly linked to various positions. The perception of spatialized audio relies on binaural cues, especially the time difference and the level difference of incident sound at a user’s left and right ears. Traditional stereo speakers cannot create the spatialization perception for a user when playing binaural audio due to auditory crosstalk, as each ear hears a combination of both speaker outputs. We present Xblock, a novel time-domain pose-adaptive crosstalk cancellation technique that creates a spatial audio perception over a pair of speakers using knowledge of the user’s head pose and speaker positions. We build a prototype smart speaker IoT system empowered by Xblock, explore the effectiveness of Xblock through signal analysis, and discuss future perceptual user studies and future work.
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When Speakers Are All Ears: Characterizing Misactivations of IoT Smart Speakers
Abstract Internet-connected voice-controlled speakers, also known as smart speakers , are increasingly popular due to their convenience for everyday tasks such as asking about the weather forecast or playing music. However, such convenience comes with privacy risks: smart speakers need to constantly listen in order to activate when the “wake word” is spoken, and are known to transmit audio from their environment and record it on cloud servers. In particular, this paper focuses on the privacy risk from smart speaker misactivations , i.e. , when they activate, transmit, and/or record audio from their environment when the wake word is not spoken. To enable repeatable, scalable experiments for exposing smart speakers to conversations that do not contain wake words, we turn to playing audio from popular TV shows from diverse genres. After playing two rounds of 134 hours of content from 12 TV shows near popular smart speakers in both the US and in the UK, we observed cases of 0.95 misactivations per hour, or 1.43 times for every 10,000 words spoken, with some devices having 10% of their misactivation durations lasting at least 10 seconds. We characterize the sources of such misactivations and their implications for consumers, and discuss potential mitigations.
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- Award ID(s):
- 1909020
- NSF-PAR ID:
- 10192512
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2020
- Issue:
- 4
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 255 to 276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Smart speaker voice assistants (VAs) such as Amazon Echo and Google Home have been widely adopted due to their seamless integration with smart home devices and the Internet of Things (IoT) technologies. These VA services raise privacy concerns, especially due to their access to our speech. This work considers one such use case: the unaccountable and unauthorized surveillance of a user's emotion via speech emotion recognition (SER). This paper presents DARE-GP, a solution that creates additive noise to mask users' emotional information while preserving the transcription-relevant portions of their speech. DARE-GP does this by using a constrained genetic programming approach to learn the spectral frequency traits that depict target users' emotional content, and then generating a universal adversarial audio perturbation that provides this privacy protection. Unlike existing works, DARE-GP provides: a) real-time protection of previously unheard utterances, b) against previously unseen black-box SER classifiers, c) while protecting speech transcription, and d) does so in a realistic, acoustic environment. Further, this evasion is robust against defenses employed by a knowledgeable adversary. The evaluations in this work culminate with acoustic evaluations against two off-the-shelf commercial smart speakers using a small-form-factor (raspberry pi) integrated with a wake-word system to evaluate the efficacy of its real-world, real-time deployment.
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null (Ed.)Successful listening in a second language (L2) involves learning to identify the relevant acoustic–phonetic dimensions that differentiate between words in the L2, and then use these cues to access lexical representations during real-time comprehension. This is a particularly challenging goal to achieve when the relevant acoustic–phonetic dimensions in the L2 differ from those in the L1, as is the case for the L2 acquisition of Mandarin, a tonal language, by speakers of non-tonal languages like English. Previous work shows tone in L2 is perceived less categorically (Shen and Froud, 2019) and weighted less in word recognition (Pelzl et al., 2019) than in L1. However, little is known about the link between categorical perception of tone and use of tone in real time L2 word recognition at the level of the individual learner. This study presents evidence from 30 native and 29 L1-English speakers of Mandarin who completed a real-time spoken word recognition and a tone identification task. Results show that L2 learners differed from native speakers in both the extent to which they perceived tone categorically as well as in their ability to use tonal cues to distinguish between words in real-time comprehension. Critically, learners who reliably distinguished between words differing by tone alone in the word recognition task also showed more categorical perception of tone on the identification task. Moreover, within this group, performance on the two tasks was strongly correlated. This provides the first direct evidence showing that the ability to perceive tone categorically is related to the weighting of tonal cues during spoken word recognition, thus contributing to a better understanding of the link between phonemic and lexical processing, which has been argued to be a key component in the L2 acquisition of tone (Wong and Perrachione, 2007).more » « less
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Abstract Issues of intelligibility may arise amongst English learners when acquiring new words and phrases in North American academic settings, perhaps in part due to limited linguistic data available to the learner for understanding language use patterns. To this end, this paper examines the effects of Data‐Driven Learning for Pronunciation (DDLfP) on lexical stress and prominence in the US academic context. 65 L2 English learners in North American universities completed a diagnostic and pretest with listening and speaking items before completing four online lessons and a posttest on academic words and formulas (i.e., multi‐word sequences). Experimental group participants (
n = 40) practiced using an audio corpus of highly proficient L2 speakers while comparison group participants (n = 25) were given teacher‐created pronunciation materials. Logistic regression results indicated that the group who used the corpus significantly increased their recognition of prominence in academic formulas. In the spoken tasks, both groups improved in their lexical stress pronunciation, but only the DDLfP learners improved their production of prominence in academic formulas. Learners reported that they valued DDLfP efforts for pronunciation learning across contexts and speakers. Findings have implications for teachers of L2 pronunciation and support the use of corpora for language teaching and learning. -
null (Ed.)People who grow up speaking a language without lexical tones typically find it difficult to master tonal languages after childhood. Accumulating research suggests that much of the challenge for these second language (L2) speakers has to do not with identification of the tones themselves, but with the bindings between tones and lexical units. The question that remains open is how much of these lexical binding problems are problems of encoding (incomplete knowledge of the tone-to-word relations) vs. retrieval (failure to access those relations in online processing). While recent work using lexical decision tasks suggests that both may play a role, one issue is that failure on a lexical decision task may reflect a lack of learner confidence about what is not a word, rather than non-native representation or processing of known words. Here we provide complementary evidence using a picture- phonology matching paradigm in Mandarin in which participants decide whether or not a spoken target matches a specific image, with concurrent event-related potential (ERP) recording to provide potential insight into differences in L1 and L2 tone processing strategies. As in the lexical decision case, we find that advanced L2 learners show a clear disadvantage in accurately identifying tone mismatched targets relative to vowel mismatched targets. We explore the contribution of incomplete/uncertain lexical knowledge to this performance disadvantage by examining individual data from an explicit tone knowledge post-test. Results suggest that explicit tone word knowledge and confidence explains some but not all of the errors in picture-phonology matching. Analysis of ERPs from correct trials shows some differences in the strength of L1 and L2 responses, but does not provide clear evidence toward differences in processing that could explain the L2 disadvantage for tones. In sum, these results converge with previous evidence from lexical decision tasks in showing that advanced L2 listeners continue to have difficulties with lexical tone recognition, and in suggesting that these difficulties reflect problems both in encoding lexical tone knowledge and in retrieving that knowledge in real time.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2478/popets-2020-0072
