diff --git a/changelog/3291.added.md b/changelog/3291.added.md
new file mode 100644
index 000000000..da7a0d47c
--- /dev/null
+++ b/changelog/3291.added.md
@@ -0,0 +1,4 @@
+- `LLMUserAggregator` now exposes the following events:
+   - `on_user_turn_started`: triggered when a user turn starts
+   - `on_bot_turn_started`: triggered when a user turn ends and a bot turn starts
+   - `on_user_turn_end_timeout`: triggered when a user turn does not stop and times out
diff --git a/src/pipecat/processors/aggregators/llm_response_universal.py b/src/pipecat/processors/aggregators/llm_response_universal.py
index 86bbd9905..6dd7f51b4 100644
--- a/src/pipecat/processors/aggregators/llm_response_universal.py
+++ b/src/pipecat/processors/aggregators/llm_response_universal.py
@@ -51,6 +51,8 @@ from pipecat.frames.frames import (
     UserImageRawFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
+    VADUserStartedSpeakingFrame,
+    VADUserStoppedSpeakingFrame,
 )
 from pipecat.processors.aggregators.llm_context import (
     LLMContext,
@@ -75,9 +77,12 @@ class LLMUserAggregatorParams:
             interruption frames. This is enabled by default, but you may want
             to disable it if another component (e.g., an STT service) is already
             generating these frames.
+        user_turn_end_timeout: Time in seconds to wait before considering the
+            user's turn finished and starting the bot turn.
     """
 
     enable_user_speaking_frames: bool = True
+    user_turn_end_timeout: float = 5.0
 
 
 @dataclass
@@ -226,15 +231,20 @@ class LLMUserAggregator(LLMContextAggregator):
 
     - on_user_turn_started: Called when the user turn starts
     - on_bot_turn_started: Called when the user turn ends and it is now the bot’s turn
+    - on_user_turn_end_timeout: Called when no bot turn start strategy triggers
 
     Example::
 
         @aggregator.event_handler("on_user_turn_started")
-        async def on_user_turn_started(aggregator, strategy):
+        async def on_user_turn_started(aggregator, Optional[strategy]):
             ...
 
         @aggregator.event_handler("on_bot_turn_started")
-        async def on_bot_turn_started(aggregator, strategy):
+        async def on_bot_turn_started(aggregator, Optional[strategy]):
+            ...
+
+        @aggregator.event_handler("on_user_turn_end_timeout")
+        async def on_user_turn_end_timeout(aggregator):
             ...
 
     """
@@ -255,9 +265,15 @@ class LLMUserAggregator(LLMContextAggregator):
         """
         super().__init__(context=context, role="user", **kwargs)
         self._params = params or LLMUserAggregatorParams()
-        self._user_speaking = False
+
+        self._vad_user_speaking = False
+
+        self._user_turn = False
+        self._user_turn_end_timeout_event = asyncio.Event()
+        self._user_turn_end_timeout_task: Optional[asyncio.Task] = None
 
         self._register_event_handler("on_user_turn_started")
+        self._register_event_handler("on_user_turn_end_timeout")
         self._register_event_handler("on_bot_turn_started")
 
     async def cleanup(self):
@@ -299,6 +315,12 @@ class LLMUserAggregator(LLMContextAggregator):
         elif isinstance(frame, CancelFrame):
             await self._cancel(frame)
             await self.push_frame(frame, direction)
+        elif isinstance(frame, VADUserStartedSpeakingFrame):
+            await self._handle_vad_user_started_speaking(frame)
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, VADUserStoppedSpeakingFrame):
+            await self._handle_vad_user_stopped_speaking(frame)
+            await self.push_frame(frame, direction)
         elif isinstance(frame, TranscriptionFrame):
             await self._handle_transcription(frame)
         elif isinstance(frame, LLMRunFrame):
@@ -335,6 +357,11 @@ class LLMUserAggregator(LLMContextAggregator):
         await self.push_context_frame()
 
     async def _start(self, frame: StartFrame):
+        if not self._user_turn_end_timeout_task:
+            self._user_turn_end_timeout_task = self.create_task(
+                self._user_turn_end_timeout_task_handler()
+            )
+
         if self.turn_start_strategies and self.turn_start_strategies.user:
             for s in self.turn_start_strategies.user:
                 await s.setup(self.task_manager)
@@ -356,6 +383,10 @@ class LLMUserAggregator(LLMContextAggregator):
         await self._cleanup()
 
     async def _cleanup(self):
+        if self._user_turn_end_timeout_task:
+            await self.cancel_task(self._user_turn_end_timeout_task)
+            self._user_turn_end_timeout_task = None
+
         if self.turn_start_strategies and self.turn_start_strategies.user:
             for s in self.turn_start_strategies.user:
                 await s.cleanup()
@@ -406,6 +437,18 @@ class LLMUserAggregator(LLMContextAggregator):
             "    )"
         )
 
+    async def _handle_vad_user_started_speaking(self, frame: VADUserStartedSpeakingFrame):
+        self._vad_user_speaking = True
+
+        # The user started talking, let's reset the user turn timeout.
+        self._user_turn_end_timeout_event.set()
+
+    async def _handle_vad_user_stopped_speaking(self, frame: VADUserStoppedSpeakingFrame):
+        self._vad_user_speaking = False
+
+        # The user stopped talking, let's reset the user turn timeout.
+        self._user_turn_end_timeout_event.set()
+
     async def _handle_transcription(self, frame: TranscriptionFrame):
         text = frame.text
 
@@ -413,6 +456,9 @@ class LLMUserAggregator(LLMContextAggregator):
         if not text.strip():
             return
 
+        # We have creceived a transcription, let's reset the user turn timeout.
+        self._user_turn_end_timeout_event.set()
+
         # Transcriptions never include inter-part spaces (so far).
         self._aggregation.append(
             TextPartForConcatenation(
@@ -442,12 +488,13 @@ class LLMUserAggregator(LLMContextAggregator):
     ):
         await self.broadcast_frame(frame_cls, **kwargs)
 
-    async def _trigger_user_turn_start(self, strategy: BaseUserTurnStartStrategy):
+    async def _trigger_user_turn_start(self, strategy: Optional[BaseUserTurnStartStrategy]):
         # Prevent two consecutive user turn starts.
-        if self._user_speaking:
+        if self._user_turn:
             return
 
-        self._user_speaking = True
+        self._user_turn = True
+        self._user_turn_end_timeout_event.set()
 
         # Reset all user turn start strategies to start fresh.
         if self.turn_start_strategies and self.turn_start_strategies.user:
@@ -462,12 +509,13 @@ class LLMUserAggregator(LLMContextAggregator):
 
         await self._call_event_handler("on_user_turn_started", strategy)
 
-    async def _trigger_bot_turn_start(self, strategy: BaseBotTurnStartStrategy):
+    async def _trigger_bot_turn_start(self, strategy: Optional[BaseBotTurnStartStrategy]):
         # Prevent two consecutive bot turn starts.
-        if not self._user_speaking:
+        if not self._user_turn:
             return
 
-        self._user_speaking = False
+        self._user_turn = False
+        self._user_turn_end_timeout_event.set()
 
         # Reset all bot turn start strategies to start fresh.
         if self.turn_start_strategies and self.turn_start_strategies.bot:
@@ -484,6 +532,19 @@ class LLMUserAggregator(LLMContextAggregator):
         # Always push context frame.
         await self.push_aggregation()
 
+    async def _user_turn_end_timeout_task_handler(self):
+        while True:
+            try:
+                await asyncio.wait_for(
+                    self._user_turn_end_timeout_event.wait(),
+                    timeout=self._params.user_turn_end_timeout,
+                )
+                self._user_turn_end_timeout_event.clear()
+            except asyncio.TimeoutError:
+                if self._user_turn and not self._vad_user_speaking:
+                    await self._call_event_handler("on_user_turn_end_timeout")
+                    await self._trigger_bot_turn_start(None)
+
 
 class LLMAssistantAggregator(LLMContextAggregator):
     """Assistant LLM aggregator that processes bot responses and function calls.