VoiceTalk: A No-Code Approach for Creating Voice-Controlled Smart Home Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article introduces VoiceTalk, a no-code approach that develops voice-controlled smart home applications without requiring programming expertise. At its core, VoiceTalk utilizes IoTtalk, an IoT application development platform for managing a diverse range of IoT devices. IoTtalk employs a two-tier microservices architecture, enabling users to define and chain applications through an intuitive drag-and-drop line interface. Leveraging its microservice architecture, VoiceTalk integrates IoTtalk with Google Home, offering a no-code solution for voice-controlled applications. VoiceTalk leverages its understanding of smart appliances in the room/house to generate specific prompts. We have compared the translation accuracy of 7 Automatic Speech Recognition (ASR) systems. We make two contributions. First, the no-code VoiceTalk platform significantly simplifies the development of Google Home-like applications. Second, by integrating ASRs with a commercial LLM such as GPT, we dramatically reduce voice-to-text translation errors, for examples, from 5.13% to 0.54% for the Web Speech API and from 2.25% to zero for Whisper Medium. For small-sized open-source LLMs such as Llama 3.2 3B, the errors are reduced to 0.72% for the Web Speech API and to zero for Whisper Medium. Furthermore, Device LLM Agent of VoiceTalk can be easily extended to integrate IoTtalk with other voice platforms, such as AWS Alexa.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it