Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usability

Nowadays, speech is becoming a more common, if not standard, interface to technology. This can be seen in the trend of technology changes over the years. Increasingly, voice is used to control programs, appliances and personal devices within homes, cars, workplaces, and public spaces through smartphones and home assistant devices using Amazon's Alexa, Google's Assistant and Apple's Siri, and other proliferating technologies. However, most speech interfaces are not accessible for Deaf and Hard-of-Hearing (DHH) people. In this paper, performances of current Automatic Speech Recognition (ASR) with voices of DHH speakers are evaluated. ASR has improved over the years, and is able to reach Word Error Rates (WER) as low as 5-6% [1][2][3], with the help of cloud-computing and machine learning algorithms that take in custom vocabulary models. In this paper, a custom vocabulary model is used, and the significance of the improvement is evaluated when using DHH speech.Comment: 6 pages, 4 figure

"Hey Siri, do you understand me?": Virtual Assistants and Dysarthria

Voice-activated devices are becoming common place: people can use their voice to control smartphones, smart vacuum robots, and interact with their smart homes through virtual assistant devices like Amazon Echo or Google Home. The spread of such voice-controlled devices is possible thanks to the increasing capabilities of natural language processing, and generally have a positive impact on the device accessibility, e.g., for people with disabilities. However, a consequence of these devices embracing voice control is that people with dysarthria or other speech impairments may be unable to control their intelligent environments, at least with proficiency. This paper investigates to which extent people with dysarthria can use and be understood by the three most common virtual assistants, namely Siri, Google Assistant, and Amazon Alexa. Starting from the sentences in the TORGO database of dysarthric articulation, the differences between such assistants are investigated and discussed. Preliminary results show that the three virtual assistants have comparable performance, with an accuracy of the recognition in the range of 50-60%

Assessing Virtual Assistant Capabilities with Italian Dysarthric Speech

The usage of smartphone-based virtual assistants (e.g., Siri or Google Assistant) is growing, and their spread was most possible by the increasing capabilities of natural language processing, and generally has a positive impact on device accessibility, e.g., for people with disabilities. However, people with dysarthria or other speech impairments may be unable to use these virtual assistants with proficiency. This paper investigates to which extent people with ALS-induced dysarthria can be understood and get consistent answers by three widely used smartphone-based assistants, namely Siri, Google Assistant, and Cortana. In particular, we focus on the recognition of Italian dysarthric speech, to study the behavior of the virtual assistants with this specific population for which there are no relevant studies available. We collected and recorded suitable speech samples from people with dysarthria in a dedicated center of the Molinette hospital, in Turin, Italy. Starting from those recordings, the differences between such assistants, in terms of speech recognition and consistency in answer, are investigated and discussed. Results highlight different performance among the virtual assistants. For speech recognition, Google Assistant is the most promising, with around 25% of word error rate per sentence. Consistency in answer, instead, sees Siri and Google Assistant provide coherent answers around 60% of times

On the Impact of Dysarthric Speech on Contemporary ASR Cloud Platforms

The spread of voice-driven devices has a positive impact for people with disabilities in smart environments, since such devices allow them to perform a series of daily activities that were difficult or impossible before. As a result, their quality of life and autonomy increase. However, the speech recognition technology employed in such devices becomes limited with people having communication disorders, like dysarthria. People with dysarthria may be unable to control their smart environments, at least with the needed proficiency; this problem may negatively affect the perceived reliability of the entire environment. By exploiting the TORGO database of speech samples pronounced by people with dysarthria, this paper compares the accuracy of the dysarthric speech recognition as achieved by three speech recognition cloud platforms, namely IBM Watson Speech-to- Text, Google Cloud Speech, and Microsoft Azure Bing Speech. Such services, indeed, are used in many virtual assistants deployed in smart environments, such as Google Home. The goal is to investigate whether such cloud platforms are usable to recognize dysarthric speech, and to understand which of them is the most suitable for people with dysarthria. Results suggest that the three platforms have comparable performance in recognizing dysarthric speech, and that the accuracy of the recognition is related to the speech intelligibility of the person. Overall, the platforms are limited when the dysarthric speech intelligibility is low (80-90% of word error rate), while they improve up to reach a word error rate of 15-25% for people without abnormality in their speech intelligibility

Automated speech audiometry:Can it work using open-source pre-trained Kaldi-NL automatic speech recognition?

A practical speech audiometry tool is the digits-in-noise (DIN) test for hearing screening of populations of varying ages and hearing status. The test is usually conducted by a human supervisor (e.g., clinician), who scores the responses spoken by the listener, or online, where a software scores the responses entered by the listener. The test has 24 digit-triplets presented in an adaptive staircase procedure, resulting in a speech reception threshold (SRT). We propose an alternative automated DIN test setup that can evaluate spoken responses whilst conducted without a human supervisor, using the open-source automatic speech recognition toolkit, Kaldi-NL. Thirty self-reported normal-hearing Dutch adults (19-64 years) completed one DIN+Kaldi-NL test. Their spoken responses were recorded, and used for evaluating the transcript of decoded responses by Kaldi-NL. Study 1 evaluated the Kaldi-NL performance through its word error rate (WER), percentage of summed decoding errors regarding only digits found in the transcript compared to the total number of digits present in the spoken responses. Average WER across participants was 5.0% (range 0 - 48%, SD = 8.8%), with average decoding errors in three triplets per participant. Study 2 analysed the effect that triplets with decoding errors from Kaldi-NL had on the DIN test output (SRT), using bootstrapping simulations. Previous research indicated 0.70 dB as the typical within-subject SRT variability for normal-hearing adults. Study 2 showed that up to four triplets with decoding errors produce SRT variations within this range, suggesting that our proposed setup could be feasible for clinical applications