diff --git a/src/dailyai/conversation_wrappers.py b/src/dailyai/conversation_wrappers.py
index bb83f1272..7f688477c 100644
--- a/src/dailyai/conversation_wrappers.py
+++ b/src/dailyai/conversation_wrappers.py
@@ -43,10 +43,10 @@ class InterruptibleConversationWrapper:
     async def speak_after_delay(self, user_speech, messages):
         await asyncio.sleep(self._delay_before_speech_seconds)
         tma_in = self._llm_context_aggregator_in(
-            messages, "user", self._my_participant_id, False
+            messages, self._my_participant_id, complete_sentences=False
         )
         tma_out = self._llm_context_aggregator_out(
-            messages, "assistant", self._my_participant_id
+            messages, self._my_participant_id
         )
 
         await self._runner(user_speech, tma_in, tma_out)
diff --git a/src/dailyai/queue_aggregators.py b/src/dailyai/queue_aggregators.py
index 545c86728..55461e29c 100644
--- a/src/dailyai/queue_aggregators.py
+++ b/src/dailyai/queue_aggregators.py
@@ -47,17 +47,17 @@ class LLMContextAggregator(AIService):
             yield frame
             return
 
+        # Ignore transcription frames from the bot
+        if isinstance(frame, TranscriptionQueueFrame):
+            if frame.participantId == self.bot_participant_id:
+                return
+
         # The common case for "pass through" is receiving frames from the LLM that we'll
         # use to update the "assistant" LLM messages, but also passing the text frames
         # along to a TTS service to be spoken to the user.
         if self.pass_through:
             yield frame
 
-        # Ignore transcription frames from the bot
-        if isinstance(frame, TranscriptionQueueFrame):
-            if frame.participantId == self.bot_participant_id:
-                return
-
         # TODO: split up transcription by participant
         if self.complete_sentences:
             self.sentence += frame.text # type: ignore -- the linter thinks this isn't a TextQueueFrame, even though we check it above