Walk through any commercial break on Pakistani television, or scroll through sponsored video ads on Instagram and TikTok, and a linguistic pattern repeats: the pitch is neither pure Urdu nor pure English. It is a deliberate mix — Urdu as the matrix language carrying the message, English insertions carrying prestige, urgency, and the call to action:

"Yeh phone best hai — limited offer, abhi order karo!" "Naya smart TV sirf Rs. 85,000 me — free delivery ke saath!"

This is not sloppy speech. In Myers-Scotton's Matrix Language Frame model, bilingual speakers systematically embed one language's content morphemes inside another language's grammatical frame, and advertisers exploit this: English words signal modernity and trigger the purchase impulse, while Urdu carries trust and cultural proximity. The persuasive force of the ad lives exactly at the switch points — the emphatic stress on "best", the rising pitch into "order now!"

The problem

AI-generated voiceovers are now cheap enough for Pakistan's small businesses to use in video ads. But every practitioner who has tried knows the failure: feed a code-switched script to a TTS engine and the voice stumbles precisely where the ad needs it to soar. The Urdu words come out with English phonemes, the stress lands on the wrong syllable, and a flat pitch contour replaces the salesman's rise.

I verified this with a live demonstration: synthesizing "Yeh product best hai, order now" through a production English TTS voice, the Urdu function words receive English phonemization, stress defaults to English patterns, and the persuasive pitch rise into the call to action is absent.

What the literature says

Code-mixed speech synthesis is an established field — but for other language pairs. Sitaram and Black (CMU) built an early Hindi-English framework and found significant listener preference for systems that correctly identify and pronounce words in each language. Joshi and Garera (2023) reached production quality for Hindi-English e-commerce voice bots with a positive comparative MOS against a commercial baseline. Mandarin-English bilingual synthesis was demonstrated at Interspeech 2020, and an ICASSP 2020 system explicitly predicted log-F0 because phonetic representations alone lack the prosody code-switched synthesis needs.

Crucially, the prosodic disruption at switch points has been measured: a Chinese-English bilingual speech study found English words inside code-switched utterances showed nearly double the pitch variability of monolingual speech — average F0 standard deviation of 51.9 Hz versus 29.8 Hz. Speakers measurably alter their prosody around switch points, and a TTS system trained on monolingual prosody misrenders exactly those regions.

The gap: no published work addresses Urdu-English switch-point prosody, and none targets advertising persuasion — despite Urdu-English mixing being the everyday reality of hundreds of millions of speakers.

The proposed study

Phase 1 — Corpus: 50 code-switched Pakistani ad scripts, 5 native bilingual speakers, switch points annotated. Phase 2 — Acoustic measurement: F0 contours via pYAAPT; compare 300ms windows around switch points against monolingual windows; synthesize the same scripts with commercial TTS engines and repeat. Phase 3 — Perceptual evaluation: ABX listening tests (n ≥ 40): naturalness (MOS) and purchase intent ratings.

Expected contributions: the first acoustic characterization of Urdu-English switch-point prosody, a public annotated corpus, quantified human–synthetic prosody gaps, and a prosody-aware synthesis recipe evaluated on purchase intent — the metric advertisers actually care about.

Honest framing

This is a research proposal — the study has not yet been executed. Findings from Hindi-English and Mandarin-English systems may partially transfer, which would narrow but not eliminate the novelty. All literature claims are verifiable through the references in the full paper.