Gemma 4 for Telephony: From Two AI Models to One – Until I Switched to Chinese
The article discusses the use of Gemma 4 for telephony, a multimodal large language model that can take audio directly as input, eliminating the need for a separate speech-to-text model. The author conducted an experiment to compare the performance of Gemma 4 with and without a speech-to-text model in English, French, and Mandarin. The results showed that the multimodal model outperformed the cascade model in English and French, but not in Mandarin, where it provided incorrect answers.
- ▪Gemma 4 can take audio directly as input, eliminating the need for a separate speech-to-text model.
- ▪The author conducted an experiment to compare the performance of Gemma 4 with and without a speech-to-text model in English, French, and Mandarin.
- ▪The multimodal model outperformed the cascade model in English and French, but not in Mandarin.
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| Original publisher | Medium |
| Canonical URL | https://medium.com/@j.y.weng/gemma-4-for-telephony-i-replaced-two-ai-models-with-one-in-my-voice-phone-agent-until-i-switched-3f1bd1c91b2c |
| Publication time | Sun, 14 Jun 2026 17:33:08 +0000 |
| Retrieval time | 2026-06-14T17:34:27.799Z |
| Last seen | 2026-06-14T17:34:27.799Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 5cZceoW1Hxfv |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Gemma 4 for Telephony: I Replaced Two AI Models With One in My Voice Phone Agent — Until I Switched to ChineseJiyao Weng9 min read·19 hours ago--ListenShareBuilding a phone agent on a multimodal LLM: dropping faster-whisper and letting Gemma 4 hear the caller directly — a response-time and reply-accuracy benchmark across English, French, and MandarinPress enter or click to view image in full sizeA telephony system using Gemma 4:12BMy voice phone agent uses two models: one to hear the caller, one to think. Gemma 4 can do both at once — so I tried deleting the speech-to-text model entirely. Across English, French, and Mandarin, here’s the head-to-head on response time and the thing that actually matters on a phone line: did it reply correctly.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
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